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.     

Using genetic algorithms to calibrate a dimethylsulfide production model in the Arctic Ocean

The global climate is intimately connected to changes in the polar oceans. The variability of sea ice coverage affects deep-water formations and large-scale thermohaline circulation patterns. The polar radiative budget is sensitive to sea-ice loss and consequent surface albedo changes. Aerosols and polar cloud microphysics are crucial players in the radioactive energy balance of the Arctic Ocean. The main biogenic source of sulfate aerosols to the atmosphere above remote seas is dimethylsulfide (DMS). Recent research suggests the flux of DMS to the Arctic atmosphere may change markedly under global warming. This paper describes climate data and DMS production (based on the five years from 1998 to 2002) in the region of the Barents Sea (30–35°E and 70–80°N). A DMS model is introduced together with an updated calibration method. A genetic algorithm is used to calibrate the chlorophyll-a (CHL) measurements (based on satellite SeaWiFS data) and DMS content (determined from cruise data collected in the Arctic). Significant interannual variation of the CHL amount leads to significant interannual variability in the observed and modeled production of DMS in the study region. Strong DMS production in 1998 could have been caused by a large amount of ice algae being released in the southern region. Forcings from a general circulation model (CSIRO Mk3) were applied to the calibrated DMS model to predict the zonal mean sea-to-air flux of DMS for contemporary and enhanced greenhouse conditions at 70–80°N. It was found that significantly decreasing ice coverage, increasing sea surface temperature and decreasing mixed-layer depth could lead to annual DMS flux increases of more than 100% by the time of equivalent CO2 tripling (the year 2080). This significant perturbation in the aerosol climate could have a large impact on the regional Arctic heat budget and consequences for global warming.     

The research of Polar sea ice and its role in climate change

As an important part of global climate system, the Polar sea ice is effccting on global climate changes through ocean surface radiation balance, mass balance, energy balance as well as the circulating of sea water temperature and salinity. Sea ice research has a centuries - old history. The many correlative sea ice projects were established through the extensive international cooperation during the period from the primary research of intensity and the boaring capacity of sea ice to the development of sea/ice/air coupled model. Based on these reseamhes, the sea ice variety was combined with the global climate change. All research about sea ice includes： the physical properties and processes of sea ice and its snow cover, the ecosystem of sea ice regions, sea ice and upper snow albedo, mass balance of sea ice regions, sea ice and climate coupled model. The simulation suggests that the both of the area and volume of polar sea ice would be reduced in next century. With the developing of the sea ice research, more scientific issues are mentioned. Such as the interaction between sea ice and the other factors of global climate system, the seasonal and regional distribution of polar sea ice thickness, polar sea ice boundary and area variety trends, the growth and melt as well as their influencing factors, the role of the polynya and the sea/air interactions. We should give the best solutions to all of the issues in future sea ice studying.     

Fine-resolution simulation of surface current and sea ice in the Arctic Mediterranean Seas

A fine-resolution model is developed for ocean circulation simulation in the National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Chinese Academy of Sciences, and is applied to simulate surface current and sea ice variations in the Arctic Mediterranean Seas. A dynamic sea ice model in elastic-viscous-plastic rheology and a thermodynamic sea ice model are employed. A 200-year simulation is performed and a dimatological average of a 10-year period (141st-150th) is presented with focus on sea ice concentration and surface current variations in the Arctic Mediterranean Seas. The model is able to simulate well the East Greenland Current, Beaufort Gyre and the Transpolar Drift, but the simulated West Spitsbergen Current is small and weak. In the March climatology, the sea ice coverage can be simulated well except for a bit more ice in east of Spitsbergen Island. The result is also good for the September scenario except for less ice concentration east of Greenland and greater ice concentration near the ice margin. The extra ice east of Spitsbergen Island is caused by sea ice current convergence forced by atmospheric wind stress.     

The progress in the study of Arctic pack ice ecology

The sea ice community plays an important role in the Arctic marine ecosystem. Because of the predicted environmental changes in the Arctic environment and specifically related to sea ice, the Arctic pack ice biota has received more attention in recent years using modem ice-breaking research vessels. Studies show that the Arctic pack ice contains a diverse biota and besides ice algae, the bacterial and protozoan biomasses can be high. Surprisingly high primary production values were observed in the pack ice of the central Arctic Ocean. Occasionally biomass maximum were discovered in the interior of the ice floes, a habitat that had been ignored in most Arctic studies. Many scientific questions, which deserve special attention, remained unsolved due to logistic limitations and the sea ice characteristics. Little is know about the pack ice community in the central Arctic Ocean. Almost no data exists from the pack ice zone for the winter season. Concerning the abundance of bacteria and protozoa, more studies are needed to understand the microbial network within the ice and its role in material and energy flows. The response of the sea ice biota to global change will impact the entire Arctic marine ecosystem and a long-term monitoring program is needed. The techniques, that are applied to study the sea ice biota and the sea ice ecology, should be improved.     

Interaction between sea ice/iceberg and ship structures: A review

For ship structural design and good maneuverability in an ice-covered sea, the local and global load of ice cover on ships should be well understood. This paper reviews the extensive work done on ice loads on ships, including： （a） Ice pressure and local load determination based on field and model tests; （b） Global ice loads on ships from full-scale field observations, model tests and numerical models under different ice conditions （level ice and pack ice） and ship operations （maneuvering and mooring）. Spe- cial attention is paid to the discrete element simulation of global ice loads on ships; and （c） Analytical solutions and numerical models of impact loads of icebergs on ships for polar navigation. Finally, research potential in these areas is discussed.     

Assimilating ASAR data for estimating soil moisture profile using an ensemble Kalman filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar （ASAR） observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter （EnKF）, the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model （DHSVM） was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were con- ducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.     

Mechanical and numerical models for sea ice dynamics on small-meso scale

On small-meso scale, the sea ice dynamic characteristics are quite different from that on large scale. To model the sea ice dynamics on small-meso scale, a new elastic-viscous-plastic （EVP） constitutive model and a hybrid Lagrangian- Eulerian （HLE） numerical method are developed based on continuum theory. While a modified discrete element model （DEM） is introduced to model the ice cover at discrete state. With the EVP constitutive model, the numerical simulation for ice ridging in an idealized rectangular basin is carried out and the results are comparable with the analytical solution of jam theory. Adopting the HLE numerical model, the sea ice dynamic process is simulated in a vortex wind field. The furthering application of DEM is discussed in details for modeling the discrete distribution of sea ice. With this study, the mechanical and numerical models for sea ice dynamics can be improved with high precision and computational efficiency.     

GLIMMER Antarctic Ice Sheet Model,an experimental research of moving boundary condition

A 3-D coupled ice sheet model, GLIMMER model is introduced, and an idealized ice sheet experiment under the EISMINT-1 criterion of moving boundary condition is presented. The results of the experiment reveal that for a steady-state ice sheet profile the characteristic curves describe the process of evolution which are accordant with theoretical estimates. By solving the coupled thermodynamics equations of ice sheet, one may find the characteristic curves which derived from the conservation of the mass, energy and momentum to the ice flow profile. At the same time, an agreement, approximate to the GLIMMER case and the confirmed theoretical results, is found. Present study is explorihg work to introduce and discuss the handicaps of EISMINT criterion and GLIMMER, and prospect a few directions of the GLIMMER model.     

Snow and sea ice thermodynamics in the Arctic： Model validation and sensitivity study against SHEBA data

Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model （HIGHTSI）. Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance.     

Temperature biases in modeled polar climate and adoption of physical parameterization schemes

An annual cycle of atmospheric variations for 1989 in the Arctic has been simulated with the Weather Research and Forecasting （WRF） model. A severe cold bias was found around a cold center in surface air temperature over the Arctic Ocean, compared with results from ERA-Interim reanalysis. Four successive numerical experiments have been carried out to find out the reasons for this. The results show that the sea ice albedo scheme has the biggest influence in summer, and the effect of the cloud microphysics scheme is significant in both summer and winter. The effect of phase transition between ice and water has the biggest influence over the region near the sea ice edge in summer, and contributes little to improvement of the severe cold bias. The origi- nal crude albedo parameterization in the surface process scheme is the main reason for the large simulated cold bias of the cold center in summer. With a different land surface scheme than in the control run, cold biases of simulated surface air temperature over the Arctic Ocean are greatly reduced, by as much as 10 K, implying that the land surface scheme is critical for polar climate simulation.     

A coupled ice-ocean ecosystem model for 1-D and 3-D applications in the Bering and Chukchi Seas

Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments： three phytoplankton （pelagic diatom, flagellates and ice algae： D, F, and Ai） , three zooplankton （copepods, large zooplankton, and microzooplankton ： ZS, ZL, ZP） , three nutrients （ nitrate ＋ nitrite, ammonium, silicon ： NO3 , NH4, Si） and detritus （Det）. The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model.     

Three Decades of Sea-Ice Variability in Jiaozhou Bay Revealed by Landsat Observations

In this study, we used Landsat images and meteorological data to examine the spatiotemporal distribution and variability of sea ice in Jiaozhou Bay(JZB) between 1986 and 2016. The results show that JZB is not always covered by sea ice in winter, but in some extreme cases, sea ice has covered more than one-third of the sea area of the bay. Sea ice in JZB has generally formed between January 1 and February 5, primarily along the coast, and gradually expanding to the central area of the bay. Both meteorological and artificial factors have played important roles in modulating the sea ice distribution. We found sea ice coverage to have been strongly correlated with the accumulated freezing-degree days nine days before the occurrence of sea ice(R2 = 0.767). North-northwest surface winds have dominated the freezing period of sea water in the JZB, and wind speed has exerted a more significant influence on the formation of sea ice when the sea ice coverage has been generally small. Additionally, artificial factors began to affect the expansion of sea ice in JZB since 2007. The construction of the Jiao-Zhou-Bay Bridge(JZBB) is believed to have retarded water flow and reduced the tidal prism, thereby leading to the formation of an ice bridge along the JZBB, which effectively prevents the southward expansion of sea ice.     

Relationship of Arctic sea ice and Northern Hemispheric 500 hPa Polar vortices

Using the NCEP/NCAR reanalysis monthly 500 hPa height data on a 2.5 latitude-longitude grid and 1°×1° sea ice data,the polar vortex area,intensity index and arctic sea ice area index are calculated respectively,and the meridional distribution,period variation and the abrupts in the long range trend are analyzed to study their relationship.The results show that the meridional distribution of sea ice and polar vortex h-ave distinctive difference,the relative positions of them are different in the eastern and western hemispheres,and exept they have periods of 4 months,quasi half year,quasi year,4-5 years and 10 years commonly,and each of them has its own respective variation as well.The sea ice area is decreasing apparently since 1980's,so is the polar vortex area,but their abrupt changge time are different totally.The area of sea ice and polar vortex has prominent positive correlation,but the relationship of sea ice intensity,polar vortex intensity,polar vortex area is complicated.     

Seasonal and inter-annual variations of the primary types of the Arctic sea-ice drifting patterns

Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure （SLP） for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary types in the Arctic Ocean： Beaufort Gyre＋Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Double Gyre Drift. These four types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre＋Transpolar Drift are spring and autumn, while the Double Gyre Drift trends to prevail in winter, especially in Feb- ruary. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arc- tic Oscillation （AO） index, with a correlation coefficient of -0.54 and 0.54 （both significant with the confident level of 99%）, re- spectively. When the AO index stays in a high positive （negative） condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise （clockwise） drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.     

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.     

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.     

Sea ice thickness measurement in spring season in Bothnian Bay using an electromagnetic induction instrument

As an important component of the cryosphere,sea ice is very sensitive to the climate change.The study of the sea ice physics needs accurate sea ice thickness.This paper presents an electromagnetic-induction(EM) technique which can be used to measure the sea ice thickness distribution efficiently,and the successful application in Bothnian Bay.Based on the electromagnetic field theory and the electrical properties of sea ice and seawater,EM technique can detect the distance between the instrument and the ice/water interface accurately,than the sea ice thickness is obtained.Contrastive analysis of the apparent conductivity data obtained by EM and the value of drill-hole at same positions allows a construction of a transformable formula of the apparent conductivity to sea ice thickness.The verification of the sea ice thickness calculated by this formula indicates that EM technique is able to get reliable sea ice thickness with average relative error of only 12%.The statistic of all ice thickness profiles shows that the level ice distribution in Bothnian Bay was 0.4-0.6 m.     

An ice-ocean model study to explore climate change mechanisms in comparison with interannual-to-decadal variability of geochemical tracers

One way to identify the mechanisms that are crucial to Arctic climate change is to use existing data that exhibit interannual-to-decadal variability in the sea ice and ocean interior due to atmospheric forcing. Since around 1960 s, valuable geochemical data of the ocean interior, together with atmospheric and sea ice data, have been analyzed and examined in a coupled ice–ocean model with an idealized configuration of the Arctic Basin. This is fundamentally driven by negative salt flux, in addition to atmospheric circulation and cooling. This strategy has a clear advantage over more sophisticated models with higher resolution that require extensive data collections for verification. Around 1990, the dominant atmospheric mode shifted from the Northern Annular Mode(NAM) to the Arctic Dipole Mode(ADM). The variability of sea ice cover was explained by these two modes sequentially and reproduced in the model. In particular, the geochemical fields indicated a movement of the Transpolar Drift Stream due to the NAM and an oscillation of the Pacific water between the Atlantic and Pacific sides due to the ADM. Both these features were reproduced reasonably well by the oceanic tracers in the model, including the time lags of about one third of the oscillation periods. Thus, this strategy can suggest methods and locations for monitoring oceanographic responses to Arctic climate change.     

Inventory and geometrical changes in small glaciers covering three Northern Patagonian summits using remote sensing and GIS techniques

In the last few decades, a large quantity of research has been performed to elucidate the current behavior of glaciers in southern Chile, especially with respect to the volumetric changes in the outlets of the Northern and Southern Patagonian Icefields (NPI and SPI, respectively). Calculations have shown a generalized thinning and withdrawal, which greatly contributes to the increase in sea level attributed to the ice melt from non-polar glaciers. However, these icefields are surrounded by many small icecaps, which have yet to be studied in detail. A precise estimation of the volume of ice located in these mountain chains could provide new information with respect to this area's exact contribution to the increase in sea level. Thus, this study presents an inventory of relatively small Northern Patagonian glaciers in the surrounding of the three summits: Mount Queulat, and the Macá and Hudson volcanoes. The study used remote sensing techniques in a GIS environment to determine the margins, surface areas, thickness changes and hypsometry for the glaciated zones. Landsat images from different dates were analyzed using standard band ratio and screen delineation techniques. Additionally, digital elevation models from different dates were compared using map algebra, calculating thickness changes. Based on the results, we propose that there are important volumetric changes in the glaciers studied, which could be explained by precipitation trends in a general context, and an influence of the glaciers' sizes in some local response. Therefore, we suggest the exact contribution of the Patagonia to the increase in sea level corresponds to a regional pattern rather than just the behavior of a single ice field.