Under the Chinese National Antarctic Research Expedition program in 2006, the annual thermal mass balance of landfast ice in the vicinity of Zhongshan Station, Prydz Bay, east Antarctica, was investigated. Sea ice formed from mid‐February onward, and maximum ice thickness occurred in late November. Snow cover remained thin, and blowing snow caused frequent redistribution of the snow. The vertical ice salinity showed a “question‐mark‐shaped” profile for most of the ice growth season, which only turned into an “I‐shaped” profile after the onset of ice melt. The oceanic heat flux as estimated from a flux balance at ice‐ocean interface using internal ice temperatures decreased from 11.8 (±3.5) W m−2 in April to an annual minimum of 1.9 (±2.4) W m−2 in September. It remained low through late November, in mid‐December it increased sharply to about 20.0 W m−2. Simulations applying the modified versions of Stefan's law, taking account the oceanic heat flux and ice‐atmosphere coupling, compare well with observed ice growth. There was no obvious seasonal cycle for the thermal conductivity of snow cover, which was also derived from internal ice temperatures. Its annual mean was 0.20 (±0.04) W m‐1 °C−1.
[1] Snow and ice thermodynamics over the Arctic Ocean were simulated applying a one-dimensional model. A number of numerical experiments in synoptic (10 days in early autumn) and seasonal (May-September) scales were carried out to investigate the impact of external forcing, snow physics, and the model resolution: the number of layers in both snow and ice ranged from 3 to 40. The model forcing was based on in situ observations carried out in 2003 during the Chinese National Arctic Research Expedition (CHINARE) as well as on forecasts and analyses of the European Centre for MediumRange Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR). The model results were compared against the results of the ECMWF and NCEP/NCAR sea ice schemes. The ECMWF operational precipitation forecasts yielded realistic seasonal snowfall, while the precipitation in NCEP/NCAR reanalysis was unrealistically large. A good result on snow thickness evolution also strongly depended on the accuracy of modeled snowmelt. A time-dependent surface albedo parameterization was critical for the seasonal evolution of snow and ice thickness. Application of 15-20 model levels in snow and ice is recommended as it (1) ensured good reproduction of the vertical snow/ice temperature profile also when solar radiation was large, (2) decreased the sensitivity of snow and ice mass balance to changes in surface albedo, (3) enabled the calculation of subsurface melting of snow and ice, and (4) reasonably reproduced the superimposed ice formation and onset of ice melt. In autumn, however, the accuracy of atmospheric forcing was more important than the model resolution.
The seasonal evolution of sea ice mass balance between the Central Arctic and Fram Strait, as well as the underlying driving forces, remain largely unknown because of a lack of observations. In this study, two and three buoys were deployed in the Central Arctic during the summers of 2010 and 2012, respectively. It was established that basal ice growth commenced between mid‐October and early December. Annual basal ice growth, ranging from 0.21 to 1.14 m, was determined mainly by initial ice thickness, air temperature, and oceanic heat flux during winter. An analytic thermodynamic model indicated that climate warming reduces the winter growth rate of thin ice more than for thick ice because of the weak thermal inertia of the former. Oceanic heat flux during the freezing season was 2–4 W m−2, which accounted for 18–31% of the basal ice energy balance. We identified two mechanisms that modified the oceanic heat flux, i.e., solar energy absorbed by the upper ocean during summer, and interaction with warm waters south of Fram Strait; the latter resulted in basal ice melt, even in winter. In summer 2010, ice loss in the Central Arctic was considerable, which led to increased oceanic heat flux into winter and delayed ice growth. The Transpolar Drift Stream was relatively weak in summer 2013. This reduced sea ice advection out of the Arctic Ocean, and it restrained ice melt because of the cool atmospheric conditions, weakened albedo feedback, and relatively small oceanic heat flux in the north.
ABSTRACT. The decrease in summer sea-ice extent in the Arctic Ocean opens shipping routes and creates potential for many marine operations. For these activities accurate predictions of sea-ice conditions are required to maintain marine safety. In an attempt at Arctic sea-ice prediction, the summer of 2010 is selected to implement an Arctic sea-ice data assimilation (DA) study. The DA system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter to assimilate Special Sensor Microwave Imager/Sounder (SSMIS) sea-ice concentration operational products from the US National Snow and Ice Data Center (NSIDC). Based on comparisons with both the assimilated NSIDC SSMIS concentration and concentration data from the Ocean and Sea Ice Satellite Application Facility, the forecasted sea-ice edge and concentration improve upon simulations without data assimilation. By the nature of the assimilation algorithm with multivariate covariance between ice concentration and thickness, sea-ice thickness fields are also updated, and the evaluation with in situ observation shows some improvement compared to the forecast without data assimilation.
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