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.

An Arctic Sea Ice Simulation Using an Ocean-Ice Coupled Model

This paper evaluates the simulation of Arctic sea ice states using an ocean-ice coupled model that employs LASG/IAP(the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/the Institute of Atmospheric Physics) Climate Ocean Model(LICOM) and the sea-ice model from the Bergen Climate Model(BCM).It is shown that the coupled model can reasonably reproduce the major characteristics of the mean state,annual cycle,and interannual variability of the Arctic sea ice concentration.The coupled model also shows biases that were generally presented in other models,such as the underestimation of summer sea ice concentration and thickness as well as the unsatisfactory sea ice velocity.Sensitivity experiments indicate that the insufficient performance of the ocean model at high latitudes may be the main reason for the biases in the coupled model.The smoother and the fake "island",which had to be used due to the model's grid in the North Pole region,likely caused the ocean model's weak performance.Sea ice model thermodynamics are also responsible for the sea ice simulation biases.Therefore,both the thermodynamic module of the sea ice component and the model grid of the ocean component need to be further improved.

Variation of Sea Ice Temperature from CHINARE 2003 and Its Application on Sea Ice Model Evaluation

Variation of vertical profiles of sea ice temperature and adjacent atmosphere and ocean temperatures were measured by ice drifting buoys deployed in the northeast Chukchi Sea as part of the 2003 Chinese Arctic Research Expedition.The buoy observations (September 2003 to February 2005) show that the cooling of the ice began in late September,propagated down through the ice,reaching the bottom of the ice in December,and continued throughout the winter.In winter 2003/04,some obvious warmings were observed in the upper portion of the ice in response to major warmings in the overlying atmosphere associated with the periodicity of storms in the northeast Chukchi Sea.It is found that the melt season at the buoy site in 2004 was about 15% longer than normal.The buoy observed vertical ice temperature profiles were used as a diagnostic for sea ice model evaluation.The results show that the simulated ice temperature profiles have large discrepancies as compared with the observations.

A coupled multi-category sea ice model and POM for Baffin Bay and the Labrador Sea

An overview of the seasonal variation of sea-ice cover in Baffin Bay and the Labrador Sea is given. A coupled ice-ocean model, CECOM, has been developed to study the seasonal variation and associated ice-ocean processes. The sea-ice component of the model is a multi-category ice model in which mean concentration and thickness are expressed in terms of a thickness distribution function. Ten categories of ice thickness are specified in the model. Sea ice is coupled dynamically and thermodynamically to the Princeton Ocean Model. Selected results from the model including the seasonal variation of sea ice in Baffin Bay, the North Water polynya and ice growth and melt over the Labrador Shelf are presented.

A study on remote sensing models of sea ice thickness by microwave radiometry

Extrapolating from the propagation theories of electromagnetic waves in a layered medium, a three-layer medium model is deduced in this paper by using microwave radiometric remote sensing technology which is suitable to first-year sea ice condition of the northern part of China seas. Comparison with in situ data indicates that for microwave wavelength of 10 cm, the coherent model gives a quite good fit result for the thickness of sea ice less than 20 cm, and the incoherent model also works well for thickness within 20 to 40 cm. Based on three theoretical models, the inversion soft ware from microwave remote sensing data for calculating the thickness of sea ice can be set up. The relative complex dielectrical constants of different types of sea ice in the Liaodong Gulf calculated by using these theoretical models and measurement data are given in this paper. The extent of their values is (0. 5-4. 0)-j(0. 07~0. 19).

Modeling turbulent heat fluxes over Arctic sea ice using a maximum-entropy-production approach

Recently,an algorithm of surface turbulent heat fluxes over snow/sea ice has been developed based on the theory of maximum entropy production(MEP),which is fundamentally different from the bulk flux algorithm(BF)that has been used in sea ice models for a few decades.In this study,we first assess how well the MEP algorithm captures the observed variations of turbulent heat fluxes over Arctic sea ice.It is found that the calculated heat fluxes by the MEP method are in good agreement with in-situ observations after considering the absorption of incoming radiation in a snow/ice surface layer with infinitesimal depth.We then investigate the effects of two different schemes(MEP vs.BF)in the sea ice model of CICE6 on simulated turbulent heat fluxes and sea ice processes in the Arctic Basin.Our results show that the two different schemes give quite different representations of seasonal variations of heat fluxes,particularly for sensible heat fluxes in summer.The heat fluxes simulated by the MEP produce weak cooling effect on the ice surface in summer,whereas the BF generates a warming effect.As a result,compared to the BF,the MEP leads to a reduced seasonal cycle of Arctic sea ice mass flux by modulating snow-to-ice conversion,basal ice growth,surface ice melt and basal ice melt.

Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes.The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume,area fraction(the ratio between melt pond area to sea ice area in a model grid),and depth of melt ponds.However,this scheme has some uncertain parameters that affect melt pond simulations.These parameters could be determined through a conventional parameter estimation method,which requires a large number of sensitivity simulations.The adjoint model can calculate the parameter sensitivity efficiently.In the present research,an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme.A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model,melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm.The parameter estimation algorithm was verified under idealized conditions.By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm,the melt pond aspect ratio parameter in CESM scheme,which is defined as the ratio between pond depth and pond area fraction,was estimated every eight days during summertime for two different regions in the Arctic.One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI).The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.

A New Prediction Model for Grain Yield in Northeast China Based on Spring North Atlantic Oscillation and Late-Winter Bering Sea Ice Cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.

MODEL OF SEA ICE BREAKUP ON SHALLOW BEACH DUE TO TIDAL FLUCTUATION

There are many factors that may lead to the breakup of shore fast ice, such as wind, wave, tide and so on. This paper mainly analyzed the ice breakup on the shallow beach due to tidal fluctuation. The theoretical model was set up and the fitting range was given. The calculated result shows that the slope angle α, the ice thickness h, and the ice length l are key factors in determining the ice breakup length l p.

An Overview of BCC Climate System Model Development and Application for Climate Change Studies

This paper reviews recent progress in the development of the Beijing Climate Center Climate System Model （BCC_CSM） and its four component models （atmosphere, land surface, ocean, and sea ice）. Two recent versions are described： BCC_CSMI.1 with coarse resolution （approximately 2.8125°× 2.8125°） and BCC_CSMI.I（m） with moderate resolution （approximately 1.125°×1.125°）. Both versions are fully cou- pled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation. Both models well simulate the concentration and temporal evolution of atmospheric CO2 during the 20th century with anthropogenic CO2 emissions prescribed. Simulations using these two versions of the BCC_CSM model have been contributed to the Coupled Model Intercomparison Project phase five （CMIP5） in support of the Intergovernmental Panel on Climate Change （1PCC） Fifth Assessment Report （AR5）. These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections. Simulations of the 20th century climate using BCC-CSMI.1 and BCC_CSMI.I（m） are presented and validated, with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales. Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed. Both BCC_CSMI.1 and BCC_CSMI.I（m） perform well when compared with other CMIP5 models. Preliminary analyses in- dicate that the higher resolution in BCC CSMI.I（m） improves the simulation of mean climate relative to BCC_CSMI.1, particularly on regional scales.