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Albedo of Coastal Landfast Sea Ice in Prydz Bay, Antarctica: Observations and Parameterization


doi: 10.1007/s00376-015-5114-7

  • The snow/sea-ice albedo was measured over coastal landfast sea ice in Prydz Bay, East Antarctica (off Zhongshan Station) during the austral spring and summer of 2010 and 2011. The variation of the observed albedo was a combination of a gradual seasonal transition from spring to summer and abrupt changes resulting from synoptic events, including snowfall, blowing snow, and overcast skies. The measured albedo ranged from 0.94 over thick fresh snow to 0.36 over melting sea ice. It was found that snow thickness was the most important factor influencing the albedo variation, while synoptic events and overcast skies could increase the albedo by about 0.18 and 0.06, respectively. The in-situ measured albedo and related physical parameters (e.g., snow thickness, ice thickness, surface temperature, and air temperature) were then used to evaluate four different snow/ice albedo parameterizations used in a variety of climate models. The parameterized albedos showed substantial discrepancies compared to the observed albedo, particularly during the summer melt period, even though more complex parameterizations yielded more realistic variations than simple ones. A modified parameterization was developed, which further considered synoptic events, cloud cover, and the local landfast sea-ice surface characteristics. The resulting parameterized albedo showed very good agreement with the observed albedo.
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  • Allison I., R. E. Brand t, and S. G. Warren, 1993: East Antarctic sea ice: Albedo, thickness distribution, and snow cover. J. Geophys. Res., 98( C7), 12 417- 12 429.10.1029/93JC006482e98dd90-db70-47da-beea-4e35d980a42b4605b4f76a5a5931a730b554d9ffe514http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F93JC00648%2Fcitedbyrefpaperuri:(9d3f31a1d5e0fe44344e251d3c2e1f2d)http://onlinelibrary.wiley.com/doi/10.1029/93JC00648/citedbyCharacteristics of springtime sea ice off East Antarctica were investigated during a cruise of the Australian National Antarctic Research Expedition in October through December 1988. The fractional coverage of the ocean surface, the ice thickness, and the snow cover thickness for each of several ice types were estimated hourly for the region near the ship. These observations were carried out continuously during the 4 weeks the ship was in the ice. Thin and young ice types were prevalent throughout the region, and the observations show a systematic increase in the total area-weighted pack ice thickness (including open water area) from only 0.2 m within 50 km of the ice edge to 0.45 m close to the coast. Ice thickness averaged over the ice-covered region only is also relatively thin, ranging from 0.35 m near the ice edge to 0.65 m in the interior. These values are probably typical of average winter thickness for the area. The average snow cover thickness on the ice increased from 0.05 m near the ice edge to 0.15 m in the interior. Average ice concentration increased from less than 6/10 near the ice edge to 8/10 in the interior. The ship-observed concentrations were in good agreement with concentrations derived from passive microwave satellite imagery except in some regions of high concentration. In these regions the satellite-derived concentrations were consistently lower than those estimated from the ship, possibly because of the inability of the satellite sensors to discriminate the appreciable percentage of very thin ice observed within the total area. Spectral albedo was measured for nilas, young grey ice, grey-white ice, snow-covered ice, and open water at wavelengths from 420 to 1000 nm. Allwave albedo was computed by using the spectral measurements together with estimates of near-infrared albedo and modeled spectral solar flux. Area-averaged albedos for the East Antarctic sea ice zone in spring were derived from representative allwave albedos together with the hourly observations of ice types. These area-averaged surface albedos increased from about 0.35 at the ice edge to about 0.5 at 350 km from the edge, remaining at 0.5 to the coast of Antarctica. The low average albedo is in part due to the large fraction of open water within the pack, but extensive fractions of almost snow-free thin ice also play an important role.
    Barry R. G., 1996: The parameterization of surface albedo for sea ice and its snow cover. Progress in Physical Geography, 20( 1), 63- 79.10.1177/030913339602000104d6cd32c08f5b65918d45ff329d4b1ac0http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F249823304_The_parameterization_of_surface_albedo_for_sea_ice_and_its_snow_coverhttp://www.researchgate.net/publication/249823304_The_parameterization_of_surface_albedo_for_sea_ice_and_its_snow_coverThe factors determining the albedo of sea ice and its snow cover, including spectral characteristics, are reviewed. The thickness, properties and fractional cover of snow are of general importance. During freeze-up, ice thickness is a major determinant and, in summer, the extent and depth of melt ponds. The effects of sky conditions and surface impurities are also examined. In situ and remote-sensing data to validate theoretical and model results are discussed. The current parameterizations adopted in atmospheric GCMs are compared and new directions described.
    Brand t, R. E., S. G. Warren, A. P. Worby, T. C. Grenfell, 2005: Surface albedo of the Antarctic sea ice zone. J. Climate, 18( 17), 3606- 3622.10.1175/JCLI3489.14115aa20fb1ff972026712cde6a98720http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JCli...18.3606Bhttp://adsabs.harvard.edu/abs/2005JCli...18.3606BIn three ship-based field experiments, spectral albedos were measured at ultraviolet, visible, and near-infrared wavelengths for open water, grease ice, nilas, young 09銇04grey09銇09 ice, young grey-white ice, and first-year ice, both with and without snow cover. From the spectral measurements, broadband albedos are computed for clear and cloudy sky, for the total solar spectrum as well as for visible and near-infrared bands used in climate models, and for Advanced Very High Resolution Radiometer (AVHRR) solar channels. The all-wave albedos vary from 0.07 for open water to 0.87 for thick snow-covered ice under cloud. The frequency distribution of ice types and snow coverage in all seasons is available from the project on Antarctic Sea Ice Processes and Climate (ASPeCt). The ASPeCt dataset contains routine hourly visual observations of sea ice from research and supply ships of several nations using a standard protocol. Ten thousand of these observations, separated by a minimum of 6 nautical miles along voyage tracks, are used together with the measured albedos for each ice type to assign an albedo to each visual observation, resulting in 0904ice-only0909 albedos as a function of latitude for each of five longitudinal sectors around Antarctica, for each of the four seasons. These ice albedos are combined with 13 yr of ice concentration estimates from satellite passive microwave measurements to obtain the geographical and seasonal variation of average surface albedo. Most of the Antarctic sea ice is snow covered, even in summer, so the main determinant of area-averaged albedo is the fraction of open water within the pack.
    Briegleb B. P., C. M. Bitz, E. C. Hunke, W. H. Lipscomb, M. M. Holland , J. L. Schramm, and R. E. Moritz, 2004: Scientific description of the sea ice component in the community climate system model,version 3. NCAR/TN-463+STR.80b7461b051f2fc931eddccab48b4ad1http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F238311850_Scientific_description_of_the_sea_ice_component_in_the_Community_Climate_System_Modelhttp://www.researchgate.net/publication/238311850_Scientific_description_of_the_sea_ice_component_in_the_Community_Climate_System_ModelPublication » Scientific description of the sea ice component in the Community Climate System Model.
    Curry J. A., J. L. Schramm, D. K. Perovich, and J. O. Pinto, 2001: Applications of SHEBA/FIRE data to evaluation of snow/ice albedo parameterizations. J. Geophys. Res.: Atmos., 106( D14), 15 345- 15 355.10.1029/2000JD9003119aa0b541b951dcdbf65c351054df13a2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2000JD900311%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2000JD900311/fullClimate models use a wide variety of parameterizations for surface albedos of the ice-covered ocean. These range from simple broadband albedo parameterizations that distinguish among snow-covered and bare ice to more sophisticated parameterizations that include dependence on ice and snow depth, solar zenith angle, and spectral resolution. Several sophisticated parameterizations have also been developed for thermodynamic sea ice models that additionally include dependence on ice and snow age, and melt pond characteristics. Observations obtained in the Arctic Ocean during 1997-1998 in conjunction with the Surface Heat Budget of the Arctic Ocean (SHEBA) and FIRE Arctic Clouds Experiment provide a unique data set against which to evaluate parameterizations of sea ice surface albedo. We apply eight different surface albedo parameterizations to the SHEBA/FIRE data set and evaluate the parameterized albedos against the observed albedos. Results show that these parameterizations yield very different representations of the annual cycle of sea ice albedo. The importance of details and functional relationships of the albedo parameterizations is assessed by incorporating into a single-column sea ice model two different albedo parameterizations, one complex and one simple, that have the same annually averaged surface albedo. The baseline sea ice characteristics and strength of the ice-albedo feedback are compared for the simulations of the different surface albedos.
    Dethloff K., A. Rinke, R. Lehmann, J. H. Christensen, M. Botzet, and B. Machenhauer, 1996: Regional climate model of the Arctic atmosphere. J. Geophys. Res., 101( D18), 23 401- 23 422.10.1029/96JD02016d17e5b741f542e614533490deaee0869http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F96JD02016%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/96JD02016/pdfABSTRACT Times Cited: 56
    Fraser A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, 2012: East Antarctic landfast sea ice distribution and variability, 2000-08. J. Climate, 25( 4), 1137- 1156.10.1175/JCLI-D-10-05032.16a46379c-4bc9-426e-8bc9-0c1b51dcce7efab81e66b0a2a0f6b2ce3898e06d3f48http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012JCli...25.1137Frefpaperuri:(f0e98c0343fd0ac569c83e2f9c67b14b)http://adsabs.harvard.edu/abs/2012JCli...25.1137FAbstract This study presents the first continuous, high spatiotemporal resolution time series of landfast sea ice extent along the East Antarctic coast for the period March 2000–December 2008. The time series was derived from consecutive 20-day cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) composite images. Fast ice extent across the East Antarctic coast shows a statistically significant (1.43% ±0.30% yr 611 ) increase. Regionally, there is a strong increase in the Indian Ocean sector (20°–90°E, 4.07% ±0.42% yr 611 ), and a nonsignificant decrease in the western Pacific Ocean sector (90°–160°E, 610.40% ±0.37% yr 611 ). An apparent shift from a negative to a positive extent trend is observed in the Indian Ocean sector from 2004. This shift also coincides with a greater amount of interannual variability. No such shift in apparent trend is observed in the western Pacific Ocean sector, where fast ice extent is typically higher and variability lower than the Indian Ocean sector. The limit to the maximum fast ice areal extent imposed by the location of grounded icebergs modulates the shape of the mean annual fast ice extent cycle to give a broad maximum and an abrupt, relatively transient minimum. Ten distinct fast ice regimes are identified, related to variations in bathymetry and coastal configuration. Fast ice is observed to form in bays, on the windward side of large grounded icebergs, between groups of smaller grounded icebergs, between promontories, and upwind of coastal features (e.g., glacier tongues). Analysis of the timing of fast ice maxima and minima is also presented and compared with overall sea ice maxima/minima timing.
    Grenfell T. C., D. K. Perovich, 1984: Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea. J. Geophys. Res.: Oceans, 89( C3), 3573- 3580.10.1029/JC089iC03p03573e585677045447db8a0ddcbff26f6875chttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FJC089iC03p03573%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/JC089iC03p03573/citedbyABSTRACT Spectral albedos and incident spectral irradiances have been measured over the wavelength range 400 to 2400 nm on the sea ice near the Naval Arctic Research Laboratory (NARL) at Pt. Barrow, Alaska. The observation interval extended from mid-May, when the ice was still relatively cold, until mid-June, when summer melting was fully established. The temporal dependence of albedo for the available surface types was obtained over this time interval showing a general decrease from snow and snow-covered ice to blue ice and melt ponds. Data were also obtained for glacier ice on the Athabasca glacier, for melting lake ice, and for certain other nonice surfaces in the vicinity of NARL. Snow and ice albedos are characteristically highest at visible wavlengths, decreasing strongly in the infrared because of the increase in absorption by ice and water. Local maxima in the spectra correspond to minima in the ice and water absorption. Variations in albedo are due primarily to differences in the vapor bubble density, crystal structure, and free water content of the upper layers of the ice. Incident spectral shortwave radiation was measured as a function of cloudiness, and the optical thickness of arctic clouds is significantly less than the thickest clouds at lower latitudes. The decrease of the infrared component relative to the visible portion of the irradiance with increasing cloud cover is determined. This can give rise to an increase in wavelength-integrated albedos of as much as 15%. Using the present data, a graphical method is outlined by which visible near-infrared satellite imagery can be used to distinguish among melt ponds, open leads, and other spring and summer sea ice surface types.
    Heil P., S. Gerland , and M. A. Granskog, 2011: An Antarctic monitoring initiative for fast ice and comparison with the Arctic. The Cryosphere Discussions, 5( 5), 2437- 2463.10.5194/tcd-5-2437-2011ccd02a1b8c8532141e0d0286dc82ae17http%3A%2F%2Fwww.oalib.com%2Fpaper%2F2726267http://www.oalib.com/paper/2726267The article presents a study that investigates the result of the interannual variability in ice and snow thickness data in the Antarctic taken from the Antarctic Fast-Ice Network (AFIN) and compare them with the same variability in the Arctic. It relates that fast-ice observations are required in the polar regions for planning and scientific research of interest groups worldwide. However, it notes that in situ and satellite-based measurements for fast-ice thickness remain a challenge.
    Hoppmann M., M. Nicolaus, P. A. Hunkeler, P. Heil, L.-K. Behrens, G. König-Langlo, and R. Gerdes, 2015: Seasonal evolution of an ice-shelf influenced fast-ice regime,derived from an autonomous thermistor chain, J. Geophys. Res.: Oceans, 120, 1703-1724, doi: 10.1002/2014JC010327.5ea074e9-84a3-479e-bed6-4aa9653926b0d58709e15f9691ad9a347a07d7f3be79http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JC010327%2Ffullrefpaperuri:(b4a6d8c61454e17285fe1f396bd1339e)/s?wd=paperuri%3A%28b4a6d8c61454e17285fe1f396bd1339e%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014jc010327%2Ffull&ie=utf-8
    Järvinen O., M. Leppäranta, 2013: Solar radiation transfer in the surface snow layer in Dronning Maud Land, Antarctica. Polar Science, 7, 1- 17.
    Lei R. B., Z. J. Li, B. Cheng, Z. H. Zhang, and P. Heil, 2010: Annual cycle of landfast sea ice in Prydz Bay, east Antarctica. J. Geophys. Res.: Oceans,115(C2), doi: 10.1029/2008JC 005223.10.1029/2008JC0052238e5edcc325e5167839a11db4c35ae3adhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008JC005223%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2008JC005223/pdfABSTRACT 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 m612 in April to an annual minimum of 1.9 (±2.4) W m612 in September. It remained low through late November, in mid-December it increased sharply to about 20.0 W m612. 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 °C611.
    Leppäranta, M., O. Järvinen, E. Lindgren, 2013: Mass and heat balance of snow patches in Basen nunatak, Dronning Maud Land in summer. J. Glaciol., 59( 218), 1093- 1105.
    Liston G. E., O. Bruland , H. Elvehoy, and K. Sand, 1999: Below-surface ice melt on the coastal Antarctic ice sheet. J. Glaciol., 45( 150), 273- 285.10.3189/0022143997933771304e88bfb312a5432a0513a5d015411e4dhttp%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Figsoc%2Fjog%2F1999%2F00000045%2F00000150%2Fart00010http://www.ingentaconnect.com/content/igsoc/jog/1999/00000045/00000150/art00010In the Jutulgryta area of Dronning Maud Land, Antarctica, subsurface melting of the ice sheet has been observed. The melting takes place during the summer months in blue-ice areas under conditions of below-freezing air and surface temperatures. Adjacent snow-covered regions, having the same meteorological and climatic conditions, experience little or no subsurface melting. To help explain and understand the observed melt-rate differences in the blue-ice and snow-covered areas, a physically based numerical model of the coupled atmosphere, radiation, snow and blue-ice system has been developed. The model comprises a heat-transfer equation which includes a spectrally dependent solar-radiation source term. The penetration of radiation into the snow and blue ice depends on the solar-radiation spectrum, the surface albedo and the snow and blue-ice grain-sizes and densities. In addition, the model uses a complete surface energy balance to define the surface boundary conditions. It is run over the full annual cycle, simulating temperature profiles and melting and freezing quantities throughout the summer and winter seasons. The model is driven and validated using field observations collected during the Norwegian Antarctic Research Expedition (NARE) 1996-97. The simulations suggest that the observed differences between subsurface snow and blue-ice melting can be explained largely by radiative and heat-transfer interactions resulting from differences in albedo, grain-size and density between the two mediums.
    Liu J. P., J. A. Curry, 2010: Accelerated warming of the Southern Ocean and its impacts on the hydrological cycle and sea ice. Proceedings of the National Academy of Sciences of the United States of America, 107( 34), 14 987- 14 992.10.1073/pnas.100333610720713736ce6025cb-0bc3-4b3b-8e8c-0d1fd8210945037446d65e813952ced443727a324257http%3A%2F%2Fmed.wanfangdata.com.cn%2FPaper%2FDetail%2FPeriodicalPaper_PM20713736refpaperuri:(c307611661ed439e4d87cb46b361871a)http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM20713736The observed sea surface temperature in the Southern Ocean shows a substantial warming trend for the second half of the 20th century. Associated with the warming, there has been an enhanced atmospheric hydrological cycle in the Southern Ocean that results in an increase of the Antarctic sea ice for the past three decades through the reduced upward ocean heat transport and increased snowfall. The simulated sea surface temperature variability from two global coupled climate models for the second half of the 20th century is dominated by natural internal variability associated with the Antarctic Oscillation, suggesting that the models' internal variability is too strong, leading to a response to anthropogenic forcing that is too weak. With increased loading of greenhouse gases in the atmosphere through the 21st century, the models show an accelerated warming in the Southern Ocean, and indicate that anthropogenic forcing exceeds natural internal variability. The increased heating from below (ocean) and above (atmosphere) and increased liquid precipitation associated with the enhanced hydrological cycle results in a projected decline of the Antarctic sea ice.
    Liu J. P., Z. Zhang, J. Inoue, and R. M. Horton, 2007: Evaluation of snow/ice albedo parameterizations and their impacts on sea ice simulations. Int. J. Climatol., 27( 1), 81- 91.10.1002/joc.13730495e3c7782af69198fe1d3e80d9cd8ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.1373%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/joc.1373/fullClimate models use a variety of snow/ice albedo parameterizations for the ice covered ocean. In this study, we applied in situ measurements (surface temperature, snow depth and ice thickness) obtained from the Surface Heat Budget of the Arctic Ocean (SHEBA) as input values to four different snow/ice albedo parameterizations (representing the spectrum of parameterizations used in stand-alone sea ice models, numerical weather prediction and regional climate models of the Arctic Basin, and coupled global climate models), and evaluated the parameterized albedos against the SHEBA observed albedo. Results show that these parameterizations give very different representations of surface albedo. The impacts of systematic biases in the input values on the parameterized albedos were also assessed. To further understand how sea ice processes are influenced by differences in the albedo parameterizations, we examined baseline sea ice characteristics and responses of sea ice to an external perturbation for the simulations of the albedo parameterizations using a stand-alone basin-scale dynamic/thermodynamic sea ice model. Results show that an albedo treatment of sufficient complexity can produce more realistic basin-scale ice distributions, and likely more realistic ice responses as climate warms. Copyright 漏 2006 Royal Meteorological Society
    Lynch A. H., W. L. Chapman, J. E. Walsh, and G. Weller, 1995: Development of a regional climate model of the western Arctic. J. Climate, 8( 6), 1555- 1570.10.1175/1520-0442(1995)008<1555:DOARCM>2.0.CO;2e1505461ecc20785eed7a285e1e246aehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1995JCli....8.1555Lhttp://adsabs.harvard.edu/abs/1995JCli....8.1555LAbstract An Arctic region climate system model has been developed to simulate coupled interactions among the atmosphere, sea ice, ocean, and land surface of the western Arctic. The atmospheric formulation is based upon the NCAR regional climate model RegCM2, and includes the NCAR Community Climate Model Version 2 radiation scheme and the Biosphere–Atmosphere Transfer Scheme. The dynamic–thermodynamic sea ice model includes the Hibler–Flato cavitating fluid formulation and the Parkinson–Washington thermodynamic scheme linked to a mixed-layer ocean. Arctic winter and summer simulations have been performed at a 63 km resolution, driven at the boundaries by analyses compiled at the European Centre for Medium-Range Weather Forecasts. While the general spatial patterns are consistent with observations, the model shows biases when the results are examined in detail. These biases appear to be consequences in part of the lack of parameterizations of ice dynamics and the ice phase in atmospheric moist processes in winter, but appear to have other causes in summer. The inclusion of sea ice dynamics has substantial impacts on the model results for winter. Locally, the fluxes of sensible and latent heat increase by over 100 W m 612 in regions where offshore winds evacuate sea ice. Averaged over the entire domain, these effects result in root-mean-square differences of sensible heat flux and temperatures of 15 W m 612 and 2°C. Other monthly simulations have addressed the model sensitivity to the subgrid-scale moisture treatment, to ice-phase physics in the explicit moisture parameterization, and to changes in the relative humidity threshold for the autoconversion of cloud water to rainwater. The results suggest that the winter simulation is most sensitive to the inclusion of ice phase physics, which results in an increase of precipitation of approximately 50% and in a cooling of several degrees over large portions of the domain. The summer simulation shows little sensitivity to the ice phase and much stronger sensitivity to the convective parameterization, as expected.
    Parkinson C. L., W. M. Washington, 1979: A large-scale numerical model of sea ice. J. Geophys. Res.: Oceans, 84( C1), 311- 337.10.1029/JC084iC01p003113ba585c0f1d3ff4bda5f2dbb8e90795ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FJC084iC01p00311%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/JC084iC01p00311/pdfWork at the National Center for Atmospheric Research has resulted in the construction of a large-scale sea ice model capable of coupling with atmospheric and oceanic models of comparable resolution. The sea ice model itself simulates the yearly cycle of ice in both the northern and the southern hemispheres. Horizontally, the resolution is approximately 200 km, while vertically the model includes four layers, ice, snow, ocean, and atmosphere. Both thermodynamic and dynamic processes are incorporated, the thermodynamics being based on energy balances at the various interfaces and the dynamics being based on the following five stresses: wind stress, water stress, Coriolis force, internal ice resistance, and the stress from the tilt of the sea surface. Although the ice within a given grid square is of uniform thickness, each square also has a variable percentage of its area assumed ice free. The model results produce a reasonable yearly cycle of sea ice thickness and extent in both the Arctic and the Antarctic. The arctic ice grows from a minimum in September, when the edge has retreated from most coastlines, to a maximum in March, when the ice has reached well into the Bering Sea, has blocked the north coast of Iceland, and has moved southward of the southernmost tip of Greenland. Maximum arctic thicknesses are close to 4 m. In the Antarctic the ice expands from a minimum in March to a maximum in late August, remaining close to the continent in the former month and extending northward of 60S in the latter month. Maximum thicknesses are about 1.4 m. The distribution of modeled ice concentrations correctly reveals a more compact ice cover in the northern hemisphere than in the southern hemisphere. Modeled ice velocities obtain both the Beaufort Sea gyre and the Transpolar Drift Stream in the arctic summer as well as the Transpolar and East Greenland Drift streams in the winter. In the Antarctic, simulated velocities reveal predominantly westerly motion north of 58S, with smaller-scale cyclonic motions closer to the continent.
    Parkinson C. L., D. J. Cavalieri, 2012: Antarctic sea ice variability and trends, 1979-2010. The Cryosphere, 6( 2), 871- 880.10.5194/tcd-6-931-201258f5541b-3eaf-4b5c-b37a-7b4b7a657b315ba1440f43e26e80fe6a437f2518b917http%3A%2F%2Fwww.the-cryosphere.net%2F6%2F871%2F2012%2Ftc-6-871-2012.pdfrefpaperuri:(516492521317418af04d7a761722d240)http://www.the-cryosphere.net/6/871/2012/tc-6-871-2012.pdfIn sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978-December 2010 reveal an overall positive trend in ice extents of 17 100 卤 2300 kmyr. Much of the increase, at 13 700 卤 1500 kmyr, has occurred in the region of the Ross Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has, like the Arctic, instead experienced significant sea ice decreases, with an overall ice extent trend of -8200 卤 1200 kmyr. When examined through the annual cycle over the 32-yr period 1979-2010, the Southern Hemisphere sea ice cover as a whole experienced positive ice extent trends in every month, ranging in magnitude from a low of 9100 卤 6300 kmyrin February to a high of 24 700 卤 10 000 kmyrin May. The Ross Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and Western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but differences in the magnitudes of the two trends identify regions with overall increasing ice concentrations and others with overall decreasing ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.
    Perovich D. K., C. Polashenski, 2012: Albedo evolution of seasonal Arctic sea ice. Geophys. Res. Lett.,39(8), doi: 10.1029/2012GL051432.10.1029/2012GL0514328f438aab36b447e5d39f7acbc6d43d51http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012GL051432%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2012GL051432/pdf[1] There is an ongoing shift in the Arctic sea ice cover from multiyear ice to seasonal ice. Here we examine the impact of this shift on sea ice albedo. Our analysis of observations from four years of field experiments indicates that seasonal ice undergoes an albedo evolution with seven phases; cold snow, melting snow, pond formation, pond drainage, pond evolution, open water, and freezeup. Once surface ice melt begins, seasonal ice albedos are consistently less than albedos for multiyear ice resulting in more solar heat absorbed in the ice and transmitted to the ocean. The shift from a multiyear to seasonal ice cover has significant implications for the heat and mass budget of the ice and for primary productivity in the upper ocean. There will be enhanced melting of the ice cover and an increase in the amount of sunlight available in the upper ocean.
    Perovich D. K., T. C. Grenfell, B. Light, and P. V. Hobbs, 2002: Seasonal evolution of the albedo of multiyear Arctic sea ice. J. Geophys. Res.,107(C10), SHE 201-1-SHE 20- 13.10.1029/2000JC00043834a1fdb82052f9f53ef0d8f8bdc11420http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2000JC000438%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/2000JC000438/abstract[1] As part of ice albedo feedback studies during the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment, we measured spectral and wavelength-integrated albedo on multiyear sea ice. Measurements were made every 2.5 m along a 200-m survey line from April through October. Initially, this line was completely snow covered, but as the melt season progressed, it became a mixture of bare ice and melt ponds. Observed changes in albedo were a combination of a gradual evolution due to seasonal transitions and abrupt shifts resulting from synoptic weather events. There were five distinct phases in the evolution of albedo: dry snow, melting snow, pond formation, pond evolution, and fall freeze-up. In April the surface albedo was high (0.8-0.9) and spatially uniform. By the end of July the average albedo along the line was 0.4, and there was significant spatial variability, with values ranging from 0.1 for deep, dark ponds to 0.65 for bare, white ice. There was good agreement between surface-based albedos and measurements made from the University of Washington's Convair-580 research aircraft. A comparison between net solar irradiance computed using observed albedos and a simplified model of seasonal evolution shows good agreement as long as the timing of the transitions is accurately determined.
    Pirazzini R., 2004: Surface albedo measurements over Antarctic sites in summer. J. Geophys. Res.: Atmos.,109(D20), doi: 10.1029/2004JD004617.10.1029/2004JD004617c95597f31d1ff40a71a262232924da2ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD004617%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2004JD004617/pdf[1] Surface albedo data from several Antarctic sites were compared to determine spatial and temporal variability in albedo. The highest degree of variability was observed at Hells Gate Station on the Ross Sea coast. The temperature close to the melting point and the reduced katabatic winds during summer allowed a strong metamorphism of the snow. At Neumayer, a coastal station by the Weddell Sea, snowfall and drifting snow were more frequent, and the surface albedo was constantly high. The albedo increased by an average of 0.07 from clear days to days with snowfall and overcast sky. Surprisingly, the hourly variation in albedo at Hells Gate Station showed a trend similar to the one observed at Neumayer Station and at Dome Concordia Station on the high plateau, when only those days with fresh snow at the surface were considered. The albedo steadily decreased during the day for solar zenith angles less than 80. Snow metamorphism, sublimation during the day, and refreezing and/or crystal formation/precipitation during the night can explain the observed trend. To represent the daily trend in albedo over ice and fresh snow, we propose two parameterizations, which can be easily applied over other Arctic and Antarctic sites in summer. Small- and large-scale surface roughness elements can result in distortion in the measured albedo. The data at Reeves N茅v茅 Station show the effect produced on the albedo by changing slightly the sampling area immediately over a sastruga.
    Pirazzini R., T. Vihma, M. A. Granskog, and B. Cheng, 2006: Surface albedo measurements over sea ice in the Baltic Sea during the spring snowmelt period. Ann. Glaciol., 44, 7- 14.6552c674e4a69b6a8f4e5cc37ce1f036http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006angla..44....7p/s?wd=paperuri%3A%289555c8b3c842d10589a0f7c13d6da8a2%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006angla..44....7p&ie=utf-8
    Smith I. J., P. J. Langhorne, T. G. Haskell, H. Joe Trodahl, R. Frew, and M. R. Vennell, 2001: Platelet ice and the land-fast sea ice of McMurdo Sound, Antarctica. Ann. Glaciol., 33, 21- 27.
    Vihma T., M. M. Johansson, and J. Launiainen, 2009: Radiative and turbulent surface heat fluxes over sea ice in the western Weddell Sea in early summer. J. Geophys. Res.: Oceans,114(C4), doi: 10.1029/2008JC004995.10.1029/2008JC00499525c169f9ece2dc37cf6ec2758f027fd4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008JC004995%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/2008JC004995/abstract[1] The radiative and turbulent heat fluxes between the snow-covered sea ice and the atmosphere were analyzed on the basis of observations during the Ice Station Polarstern (ISPOL) in the western Weddell Sea from 28 November 2004 to 2 January 2005. The net heat flux to the snowpack was 3 ± 2 W m 612 (mean ± standard deviation; defined positive toward snow), consisting of the net shortwave radiation (52 ± 8 W m 612 ), net longwave radiation (6129 ± 4 W m 612 ), latent heat flux (6114 ± 5 W m 612 ), and sensible heat flux (616 ± 5 W m 612 ). The snowpack receives heat at daytime while releases heat every night. Snow thinning was due to approximately equal contributions of the increase of snow density, melt, and evaporation. The surface albedo only decreased from 0.9 to 0.8. During a case of cold air advection, the sensible heat flux was even below 6150 W m 612 . At night, the snow surface temperature was strongly controlled by the incoming longwave radiation. The diurnal cycle in the downward solar radiation drove diurnal cycles in 14 other variables. Comparisons against observations from the Arctic sea ice in summer indicated that at ISPOL the air was colder, surface albedo was higher, and a larger portion of the absorbed solar radiation was returned to the atmosphere via turbulent heat fluxes. The limited melt allowed larger diurnal cycles. Due to regional differences in atmospheric circulation and ice conditions, the ISPOL results cannot be fully generalized for the entire Antarctic sea ice zone.
    Warren S. G., 1982: Optical properties of snow. Rev. Geophys., 20( 1), 67- 89.10.1029/RG020i001p0006770efb37b-eb58-40f8-9051-939a291b609f8797460358526d8b3654f8389550341bhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FRG020i001p00067%2Fabstractrefpaperuri:(0b77a1f50062260f5f3f609ccd9e8726)http://onlinelibrary.wiley.com/doi/10.1029/RG020i001p00067/abstractMeasurements of the dependence of snow albedo on wavelength, zenith angle, grain size, impurity content, and cloud cover can be interpreted in terms of single-scattering and multiple-scattering radiative transfer theory. Ice is very weakly absorptive in the visible (minimum absorption at = 0.46 m) but has strong absorption bands in the near infrared (near IR). Snow albedo is therefore much lower in the near IR. The near-IR solar irradiance thus plays an important role in snowmelt and in the energy balance at a snow surface. The near-IR albedo is very sensitive to snow grain size and moderately sensitive to solar zenith angle. The visible albedo (for pure snow) is not sensitive to these parameters but is instead affected by snowpack thickness and parts-per-million amounts (or less) of impurities. Grain size normally increases as the snow ages, causing a reduction in albedo. If the grain size increases as a function of depth, the albedo may suffer more reduction in the visible or in the near IR, depending on the rate of grain size increase. The presence of liquid water has little effect per se on snow optical properties in the solar spectrum, in contrast to its enormous effect on microwave emissivity. Snow albedo is increased at all wavelengths as the solar zenith angle increases but is most sensitive around =1 m. Many apparently conflicting measurements of the zenith angle dependence of albedo are difficult to interpret because of modeling error, instrument error, and inadequate documentation of grain size, surface roughness, and incident radiation spectrum. Cloud cover affects snow albedo both by converting direct radiation into diffuse radiation and also by altering the spectral distribution of the radiation. Cloud cover normally causes an increase in spectrally integrated snow albedo. Some measurements of spectral flux extinction in snow are difficult to reconcile with the spectral albedo measurements. The bidirectional reflectance distribution function which apportions the reflected solar radiation among the various reflection angles must be known in order to interpret individual satellite measurements. It has been measured at the snow surface and at the top of the atmosphere, but its dependence on wavelength, snow grain size, and surface roughness is still unknown. Thermal infrared emissivity of snow is close to 100% but is a few percent lower at large viewing angles than for overhead viewing. It is very insensitive to grain size, impurities, snow depth, liquid water content, or density. Solar reflectance and microwave emissivity are both sensitive to various of these snowpack parameters. However, none of these parameters can be uniquely determined by satellite measurements at a single wavelength; a multichannel method is thus necessary if they are to be determined by remote sensing.
    Weiss A. I., J. C. King, T. A. Lachlan-Cope, and R. S. Ladkin, 2012: Albedo of the ice covered Weddell and Bellingshausen Seas. The Cryosphere, 6, 479- 491.10.5194/tc-6-479-2012a2d8c0930f93eddb43d0d768ec71ea64http%3A%2F%2Fwww.oalib.com%2Fpaper%2F2720797http://www.oalib.com/paper/2720797The article presents a study which examines the surface albedo of the sea ice areas which are closer to the Antarctic Peninsula during the austral summer. It notes that the averaged surface albedo deviated between 0.13 and 0.81. Also, it mentions that the ice cover of the Bellingshausen Sea contains the first year ice and its sea surface depicted an averaged sea ice albedo of 0.64.
    Wendler G., B. Moore, D. Dissing, and J. Kelley, 2000: On the radiation characteristics of Antarctic sea ice. Atmos.-Ocean, 38( 2), 349- 366.10.1080/07055900.2000.9649652eb0418d133385601b69a1b1b02ce1944http%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.1080%2F07055900.2000.9649652http://www.tandfonline.com/doi/abs/10.1080/07055900.2000.9649652Radiative measurements were carried out continuously during a cruise from Australia to Antarctica during austral summer 1995/96. Both shortwave and longwave radiative fluxes were measured. Some of the results are:
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Manuscript received: 06 May 2015
Manuscript revised: 22 September 2015
Manuscript accepted: 12 October 2015
通讯作者: 陈斌, bchen63@163.com
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Albedo of Coastal Landfast Sea Ice in Prydz Bay, Antarctica: Observations and Parameterization

  • 1. Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081
  • 2. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven 27570, Germany
  • 3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 4. University of Helsinki, Helsinki 00014, Finland
  • 5. University of Bremen, Bremen 28334, Germany
  • 6. Key Laboratory for Polar Science of the State Oceanic Administration, Polar Research Institute of China, Shanghai 200136
  • 7. National Center of Ocean Standards and Metrology, Tianjin 300112

Abstract: The snow/sea-ice albedo was measured over coastal landfast sea ice in Prydz Bay, East Antarctica (off Zhongshan Station) during the austral spring and summer of 2010 and 2011. The variation of the observed albedo was a combination of a gradual seasonal transition from spring to summer and abrupt changes resulting from synoptic events, including snowfall, blowing snow, and overcast skies. The measured albedo ranged from 0.94 over thick fresh snow to 0.36 over melting sea ice. It was found that snow thickness was the most important factor influencing the albedo variation, while synoptic events and overcast skies could increase the albedo by about 0.18 and 0.06, respectively. The in-situ measured albedo and related physical parameters (e.g., snow thickness, ice thickness, surface temperature, and air temperature) were then used to evaluate four different snow/ice albedo parameterizations used in a variety of climate models. The parameterized albedos showed substantial discrepancies compared to the observed albedo, particularly during the summer melt period, even though more complex parameterizations yielded more realistic variations than simple ones. A modified parameterization was developed, which further considered synoptic events, cloud cover, and the local landfast sea-ice surface characteristics. The resulting parameterized albedo showed very good agreement with the observed albedo.

1. Introduction
  • Satellite passive-microwave observations show that the total area of Antarctic sea ice in winter has been increasing by 171002300 km2 yr-1 since the late 1970s (e.g., Liu and Curry, 2010; Parkinson and Cavalieri, 2012). To understand and model the variability of Antarctic sea ice cover, accurate knowledge and parameterization of the albedo are needed. To date, most in-situ studies of solar radiation in polar seas have been carried out in the Arctic (e.g., Perovich et al., 2002; Perovich and Polashenski, 2012), while very limited observations of radiative characteristics and optical properties (especially albedo) have been performed over Antarctic sea ice (e.g., Allison et al., 1993; Brandt et al., 2005; Vihma et al., 2009; Weiss et al., 2012). Moreover, all the existing observational records over Antarctic sea ice are too short and do not cover the seasonal evolution and interannual variability, and sea-ice conditions around Antarctica differ significantly from those in the Arctic (Wendler et al., 2000).

    Figure 1.  Location of landfast sea ice surface measurements near Zhongshan Station in 2010 and 2011. The solid triangle denotes the observation site, the solid circle marks Zhongshan Station, and the solid squares refer the locations of Zhongshan, Mawson, Davis and Progress II stations.

    The albedo of snow and sea ice is a complex function of surface layer characteristics and illumination conditions, which depend on the atmospheric cloudiness and humidity and the solar zenith angle. Snow and sea ice have high reflectance in the visible band but they are moderately absorptive in the near infrared (Curry et al., 2001). The snow albedo depends on the grain size and shape, snow thickness and optical properties of its underlying surface (Grenfell and Perovich, 1984; Leppäranta et al., 2013). The sea-ice albedo is also affected by ice types (e.g., new ice, first-year ice, multiyear ice, ridged ice, brash ice), ice thickness, brine pockets and gas bubbles in the ice, surface roughness, and melt ponds (Perovich et al., 2002). Due to surface-layer evolution, the snow/ice albedo shows obvious seasonal variation, i.e., the albedo is highest in the dry snow period and significantly reduced in the melting period (Perovich et al., 2002; Perovich and Polashenski, 2012). Weather events, such as snowfall and blowing snow, can affect the albedo by changing the surface properties (Brandt et al., 2005). Cloud cover can change the directional distribution of incident solar radiation and therefore increase the snow/ice surface albedo (Curry et al., 2001). The albedo may also change with solar zenith angle (Pirazzini, 2004).

    Currently, global climate models use a number of snow/ sea-ice albedo parameterizations with different complexities (e.g., Barry, 1996; Curry et al., 2001; Liu et al., 2007). However, sea-ice albedo parameterizations have been largely determined and validated with observations that have been collected in the Arctic (e.g., Curry et al., 2001; Liu et al., 2007), and little is known about how well these parameterizations perform for Antarctic sea ice (Weiss et al., 2012), especially the complex parameterizations that consider snow thickness and ice thickness. Thus, continuous time series of the Antarctic sea ice albedo are required to quantify the influence of different factors on variations in albedo, and provide a dataset for evaluating and developing Antarctic sea-ice albedo parameterizations.

    Antarctic landfast sea ice is an important connection between the ice sheet and pack ice/ocean (Fraser et al., 2012) and has gained much attention in recent years. Because of its immobility, repeated measurements of near-coastal landfast sea ice are a feasible way to examine the temporal evolution of Antarctic sea ice (e.g., Smith et al., 2001; Lei et al., 2010; Heil et al., 2011; Hoppmann et al., 2015). The coastal landfast sea ice in Prydz Bay, East Antarctica, is normally first-year sea ice, crushed and melted completely in January or February and refrozen by the end of February or early March; so, the ice-free period is generally less than 1 month in duration (Lei et al., 2010). To improve our understanding of albedo variation over Antarctic sea ice, we carried out radiation measurements on the landfast sea ice at a fixed site near Zhongshan Station in Prydz Bay. This site has been operational every year in austral spring and early summer since 2010.

    In this paper, in-situ measurements of surface albedo near Zhongshan Station in 2010 and 2011 are analyzed. We describe the variations in albedo over Antarctic coastal landfast sea ice during austral spring and early summer. We then investigate the primary factors influencing the variations in observed albedo, particularly the effects of synoptic events, including snowfall, blowing snow and overcast skies. Finally, we evaluate the commonly used albedo parameterizations with different levels of complexity to determine to what extent they can produce the observed albedo.

2. Observation site and measurement description
  • The site of the sea-ice radiation measurements is located in the coastal area off Zhongshan Station [(69°22'S, 76°22'E); Fig. 1]. Meanwhile, the meteorological data were collected at a manned weather station, which is 1 km inland from the sea ice observation site at 15 m above the sea level. Solid precipitation is measured every 12 hours at the Russian Progress II station (located 1 km from the sea ice observation site). The meteorological conditions during the period of sea-ice observations in 2010 and 2011 are shown in Fig. 2. In 2010, the surface air temperature was below 0°C before 9 December; whereas, in 2011, most of the daily maximum air temperatures exceeded 0°C after 11 November (Fig. 2a). As a result, surface melting in 2011 started around 20 days earlier than that in 2010. The relative humidity was generally low (mostly below 60%; Fig. 2b), which is typical for the coast of the Antarctic continent. The mean snowfall amount was lower in 2011 than in 2010, i.e., the total solid precipitation in August, September, October and November of 2010/2011 was 46.9/14.7, 15.9/17.2, 7.7/5.2, and 12.5/0.4 mm SWE (snow water equivalent), respectively (Fig. 2c). The mean cloudiness for August, September, October and November in the two years ranged from 48% to 85%, but there were more clear-sky days and fewer cloudy days in 2011 than in 2010 (Fig. 2d). The mean wind speed (Fig. 2e) ranged from 4.8 m s-1 to 8.3 m s-1, and the surface wind direction was primarily northeast (approximately 77.1% of the observation time).

    Figure 2.  Time series of (a) hourly surface air temperature, (b) hourly surface relative humidity, (c) daily solid precipitation (in water equivalent depth), (d) daily observed mean cloudiness and (e) hourly surface wind speed. The red coloring denotes 2010 (1 August to 15 December) and the blue coloring denotes 2011 (1 August to 30 November).

    Figure 3.  Time series of (a) albedo for solar zenith angles less than 80$^\circ$, (b) surface temperature, (c) daily snow thickness, and (d) daily ice thickness from 25 August to 15 December, 2010, and from 25 August to 30 November, 2011. The red and blue lines (dots) represent the results from 2010 and 2011, respectively.

    Four broadband components of radiation, including downward and upward shortwave ( Sw in/ Sw out) and longwave ( Lw in/ Lw out) fluxes, were measured over the landfast sea ice from 27 July to 15 December 2010 and from 1 August to 30 November 2011.

    The shortwave and longwave radiation were measured with a net radiometer mounted at 1.5 m above the surface on a 3-m high tripod (Fig. 1, left panel). The net radiometer includes a pyranometer and a pyrgeometer. The pyranometer measures incoming and outgoing shortwave radiation, and the pyrgeometer measures downward and upward longwave radiation. The spectral bands of the pyranometer are 310-2800 nm, and the spectral bands of the pyrgeometer are 4500-42 000 nm.

    The fluxes were recorded every minute. The domes of the radiation sensors were checked between 1230 and 1300 LST (local time) every day, and ice, snow and/or frost flower cover was seldom observed during the campaign. Because surface melt might cause some tilting of the instrument, the horizontal levelling of the radiometers was also checked and adjusted if necessary. The uncertainty associated with the radiation measurements is 5%. We calculated snow/bare-ice surface temperature from the downward/upward longwave fluxes with a fixed emissivity following the method of (Pirazzini et al., 2006). Because albedo measurements are not reliable under large solar zenith angles (Vihma et al., 2009), the albedo was calculated from the ratio Sw in/ Sw out for zenith angles lower than 80°, which is consistent with (Pirazzini, 2004) and (Järvinen and Leppäranta, 2013).

    Snow thickness was measured almost every day using a ruler with an accuracy of 0.2 cm. A rough estimation of the snow grain size at the surface was made visually using a scaled magnifier. To avoid disturbances to the albedo measurements, the snow observations were made near the view of the downward-facing pyranometer but with snow characteristics similar to the surface below the radiometers. Sea ice thickness was measured with an ice auger (5 cm in diameter) every 7 days by averaging the data obtained from three close sites. The measurement accuracy of ice thickness was 0.5 cm.

    As the snow thickness and other surface properties are measured manually at noon time, the albedo at 1200 LST was used in this study, and the zenith angle at local noon was lower than 80° from 25 August onward in both 2010 and 2011.

3. Evolution of the observed albedo, atmospheric condition and surface properties
  • Time series of the incoming and outgoing shortwave radiation, albedo, surface temperature, snow thickness, and ice thickness from 25 August to 15 December 2010 and from 25 August to 30 November 2011 are shown in Fig. 3. In response to the spring-summer transition, both shortwave and longwave radiation components increased steadily (figure not shown).

  • The daily averaged surface temperature varied from -26.0°C to -0.2°C, and the daily maximum was slightly above 0°C after 3 December (Fig. 3b). The ice surface was covered (or partially covered) with snow during the entire observation period. The mean snow thickness during the observation period was 4.0 cm, but it was below 2.0 cm during most of the observation period. Snow thickness was larger than 10 cm during 15-27 September and 3 November, with the largest value of 29 cm on 15 September (Fig. 3c). Dominated by sea ice thermodynamics, the ice thickness increased from 133 2 cm on 28 August to 176 2 cm 10 November——rapidly between mid-September and the end of October. The ice thickness in November is close to the thermodynamic equilibrium between heat loss to the atmosphere and heat gain from ocean water, latent heat of freezing, solar heat flux and air-sea interaction. Finally, the ice thickness was 173 2 cm on 8 December (Fig. 3d).

    The mean albedo was 0.70 during the 2010 campaign. The surface was under metamorphism with the air temperature increasing, and the monthly mean albedo decreased from 0.80 in September to 0.62 in December. The highest albedos were at times the snow layer was thick, during snowfall and blowing-snow events. There were 26 days with a noon albedo higher than 0.8. Taking the period 13 September to 28 September as an example, an intermittent snowfall associated with snowstorms occurred. When the surface was covered with a thin layer of fresh dry snow (snow thickness of 3.0 cm), the albedo was 0.83 on 13 September, and from 14-16 September, with the new snowfall, it was higher than 0.9. The albedo peaked at 0.94 on 15 September, when the new, fresh snow accumulation was 29 cm thick and with a grain size smaller than 1 mm. Because of the low surface air temperature and strong winds (Fig. 2a), a hard frozen layer formed at the surface on 18 September. As a result, the albedo decreased to 0.81 and remained at this level until 24 September, when a new 1-cm fresh snow layer increased the albedo to 0.86. The surface was covered with a hard frozen layer again on 25-28 September, and the albedo was within 0.76-0.83. A strong wind on 28-29 September blew away most of the snow cover, and the albedo decreased to 0.68.

    There were 24 days with a noon albedo lower than 0.6; most of these measurements were associated with a thin, hard snow layer or melting snow surface (0-1 cm snow thickness). Specifically, there was no snowfall during 1-8 October, and the surface was covered with 0-1 cm of hard snow. The albedo under clear and overcast skies was 0.58-0.60 and 0.62-0.65, respectively. During 12-23 November, the surface was covered by a 0-1 cm thin wet snow layer with grains that were wet and soft and 1-3 mm in size, and then the albedo under clear and overcast skies was 0.51-0.54 and 0.56-0.63, respectively. On 6-13 December, the surface air temperature was usually above 0°C and the albedo varied between 0.49 and 0.59 (except for 9 December, when it was 0.69), which signified a surface of approximately 70% melting snow (1.0-1.2 cm) and 30% bare ice. Because the surface snow and ice were just under the melting temperature, there was a 0-1 cm surface wet snow layer. The albedo under clear and overcast skies during 6-13 December was 0.50-0.51 and 0.54-0.63, respectively, although a slight increase to 0.7 was noted on 9 December due to weak snowfall. The minimum albedo of 0.46 was observed in the afternoon of 12 December when it was under clear sky.

  • Both snow thickness and surface albedo were much smaller than in 2010, which can be partly attributed to less snowfall and more clear skies in 2011 than in 2010 (Fig. 2). In 2011, the surface was bare ice during most of the observation period, and the mean snow thickness was 0.8 cm, with most of the observation period without snow cover, which led to a mean albedo of 0.49. Over the 99-day observation period, there were 55 days with an albedo below 0.45. And the minimum albedo was as low as 0.36 in mid-November, when the surface layer was melting. Our observations showed an albedo of 0.41 for bare ice under a clear sky, which is 0.08 lower than (Brandt et al., 2005). Possible reasons contributing to the difference include the fact that our observations were based on the 1200 LST value when the solar zenith angle was near its daily minimum (the albedo was also near its daily minimum), while the observation time of (Brandt et al., 2005) was random. A 0.15 inter-diurnal variation was shown in our observations, and (Vihma et al., 2009) also observed a similar variation (0.14); the difference of 0.08 is among the range of inter-diurnal variation. Our values were based on direct measurements on the ice surface, while (Brandt et al., 2005) used ship-born sensors. There was also a difference in the spectral bands. The measurements of (Brandt et al., 2005) covered the wavelength regions of 320-1060 nm (Voyage 1988 and 1996) and 320-1800 nm (with a gap of 1000-1115 nm in Voyage 2000), while our measurement range was 310-2800 nm. Furthermore, they summarized the albedo from numerous types of sea-ice surface types in the Southern Ocean (Brandt et al., 2005), while our observation site was fixed in a local coastal landfast sea-ice zone near Zhongshan Station.

    During the measurement period of 2011, the ice thickness increased from 167 2 cm on 24 August to the maximum of 186 2 cm on 30 October, and remained at this level until 20 November when melt slowly started. The daily average surface temperature increased from -28.9°C to -0.7°C, while the instantaneous surface temperature in the afternoon reached the melting point on 14 November, 20 days earlier than that in 2010 (Fig. 3).

    There were six snowfall or blowing-snow (blizzard) events, which led to a snow accumulation of more than 2 cm, resulting in a relatively high albedo (>0.6). The highest albedo (0.94) was observed during a snowfall event on 15 October. One month earlier, after a snowfall event that started on 16 September, the maximum dry snow thickness (5.3 cm) occurred on 18 September, corresponding to a high albedo of 0.83. The snow thickness was greater than 4 cm between 18 and 24 September, and the albedo was within 0.77-0.83. The fresh snow brought by the other five falling/blowing-snow events (6-7 September, 14-15 October, 24 October, 3 November and 9 November) was blown away shortly after its accumulation, and each of the five relatively high albedo periods lasted only 1-2 days.

  • Clearly, snowfall, blowing snow and other synoptic events have a significant influence on the albedo by changing surface properties (Grenfell and Perovich, 1984), i.e., snowfall can increase the albedo by increasing snow thickness and bringing new surface snow particles.

    Figure 4.  Daily average observed surface albedo and parameterized surface albedos (a) from 25 August to 15 December, 2010 and (b) from 25 August to 30 November, 2011 (the averaged albedo for the four parameterizations over the entire period is shown in parentheses).

    We compared the albedo under snowfall with the albedo under a clear sky (or less cloudiness) on adjacent days during the 2010 observation period, and there were 14 pairs in total. This albedo increased from 0.13 to 0.29, and with an average of 0.18 among all the 14 pairwise comparisons. From August to December 2010, the total number of snowfall days at Zhongshan Station reached 67, which accounted for 44.7% of the total days and showed that frequent snowfall events have a major effect on the albedo. In contrast, our field observations suggest that, on the one hand, blowing snow (gales) may increase the surface snow thickness and reduce the surface grain size, but on the other hand, the wind may blow away all the accumulated snow at the surface. Consequently, topography and wind direction are also factors affecting albedo during blowing-snow events, and it is very difficult to add these effects into parameterization schemes.

    Clouds can absorb infrared solar radiation, and snow/ice has strong absorption in the infrared band. Because of multiple reflections, the albedo under cloudy skies is higher than that under clear skies (Warren, 1982). (Grenfell and Perovich, 1984) showed that the albedo under a cloudy sky is 5%-10% higher than that under a clear sky. We compared the albedo of the same ice and snow surfaces on adjacent days of clear (or less cloudy) days with 100% cloudy days. The albedo under a cloudy sky was higher than that under a clear sky, and the difference ranged from 0.03 to 0.09 with an average of 0.06. (Brandt et al., 2005) suggested that for the Antarctic sea ice, the dry snow and wet snow albedos were 0.07 and 0.06 higher under a cloudy sky than under a clear sky, respectively. Our observations are consistent with these results. The fraction of days during which there was full cloud cover reached 53.1% and 43.6% in the observation periods of 2010 and 2011, respectively; thus, the effects of clouds should also be considered in parameterization schemes.

4. Evaluation of albedo parameterizations in climate models
  • Following (Liu et al., 2007), four existing snow/ice albedo parameterizations ranging from simple to complex were evaluated in this study. The parameterization of Parkinson and Washington (1979) only considers broadband albedo for snow and bare ice (PW79). The Alfred Wegener Institute Regional Climate Model for the Arctic Region (Dethloff et al., 1996; HIRHAM) considers the dependence of albedo on surface temperature. The Arctic Regional Climate System Model (Lynch et al., 1995; ARCSYM) includes the impacts of snow thickness and ice thickness on the albedo. Besides weighting snow and sea ice albedos with snow thickness, the albedo scheme used in CCSM3 further distinguishes the visible and near-infrared albedos (Briegleb et al., 2004). For a detailed description of these parameterizations, see (Parkinson and Washington, 1979), (Lynch et al., 1995), (Dethloff et al., 1996), (Briegleb et al., 2004) and (Liu et al., 2007).

    Figure 4 shows the observed and simulated albedos during the measurement period. The observed surface conditions from Zhongshan Station (surface temperature, air temperature, snow thickness, and ice thickness) were used to calculate the albedos. The PW79 parameterization overestimated the observed mean albedo, both in 2010 (+0.05) and in 2011 (+0.09), and failed to capture the observed albedo variations in 2010.

    The HIRHAM parameterization underestimated the observed mean albedo in 2010 (-0.06) but overestimated the albedo in 2011 (+0.14). HIRHAM did not match the observed albedo when the surface temperature was below the melting point. In fact, it reached a minimum when the observations reached their maximum on 15 September 2010 (Fig. 4a). However, HIRHAM could reproduce the observed gradual variation in albedo, when the surface temperature approached the melting point (Figs. 4a and b). Because the surface temperature is the only dependent parameter, the HIRHAM scheme could not reproduce the observed increase in albedo during snowfall events. Additionally, as surface temperature is near the melting point during the summer period, the parameterized albedo tended to oscillate by approximately 0.30 (Figs. 4a and b). This underestimates albedo, especially when the surface is covered by a snow layer (e.g., in 2010).

    By considering the surface temperature, snow thickness and ice thickness, the ARCSYM parameterization captured the observed seasonal transitions in albedo during both spring and summer melting periods (Figs. 4a and b). The ARCSYM scheme produced a good result in summer 2010 but greatly overestimated the albedo during the 2011 melting period, e.g., the ARCSYM albedo in 2011 was always higher than 0.5, whereas the observed albedo minimum was lower than 0.4. In addition, it could not reproduce the observed rapid drop-off associated with sea ice melt.

    For the most complex case, the CCSM3 scheme showed the best result among the four parameterizations. The mean bias was -0.03 in 2010 and 0.08 in 2011. However, CCSM3 did not produce an albedo higher than 0.80 and highly overestimated the bare ice albedo in 2011. Also, it did not reproduce the observed rapid drop-off during the melting period.

    The correlations between the observed albedo and the surface parameters (surface temperature, snow thickness or ice thickness) measured near Zhongshan Station were calculated to determine the most important factors affecting albedo. The correlation coefficients between albedo and snow thickness in 2010 and 2011 were 0.55 and 0.89, while the correlation coefficients between albedo and ice thickness were -0.43 and -0.16, respectively. This suggests that albedo and ice thickness are not correlated significantly when the ice is thicker than the optical thickness of ice. The result is consistent with former studies (Barry, 1996; Curry et al., 2001; Liu et al., 2007). The correlations between albedo and surface temperature in 2010 and 2011 were -0.27 and -0.30, respectively. However, this negative correlation between albedo and surface temperature was very weak before 1 November, when surface temperature was far below the melting point; the correlations in 2010 and in 2011 were 0.42 and -0.10, respectively. There was a strong negative correlation between albedo and surface temperature after 1 November, when surface temperature approached the melting point gradually; their correlation coefficients in 2010 and 2011 were -0.49 and -0.61, respectively. The albedo dependence on surface temperature is related to the fact surface properties change as surface temperature approaches 0°C (Liston et al., 1999; Pirazzini, 2004). After removing the effects of snow thickness, the partial correlations of the albedo and surface temperature were -0.28 and -0.25 in 2010 and 2011, respectively. In contrast, the partial correlations of the albedo and snow thickness were still high (0.56 and 0.89). This further indicates that snow thickness plays the more important role in determining albedo.

    To develop a parameterization that is suitable for Antarctic landfast sea ice, we made three modifications to the CCSM3 parameterization (Table 1). In CCSM3, the bare-ice albedos in the visible and near-infrared bands were 0.73 and 0.33 (Table 1), respectively, while (Brandt et al., 2005) reported characteristic values of 0.67 and 0.29 (<0.7 and >0.7 μm), respectively. As discussed in section 3.2, our observations show a characteristic value of 0.41 for bare ice under clear sky in 2011, which is 0.08 lower than the value of (Brandt et al., 2005). Assuming that the difference in our data is independent of spectral band, subtracting 0.08 from these values suggests visual and near-infrared albedo values of 0.59 and 0.21 (Table 1, row 1). This modification is helpful for simulations of low albedo over bare ice or melting ice.

    In addition, snowfall events can influence albedo remarkably. As discussed previously, snowfall and 100% cloud cover may result in an increase in albedo by 0.18 and 0.06, respectively. The CCSM3 parameterization can only reflect the effects of changes in snow-cover thickness. In addition to snow thickness, we assume that other effects can contribute a 50% increase (0.09), and a simple formula that describes the albedo increase caused by snowfall and cloud cover is proposed (Table 1, row 2). This adjustment is important for the simulation of a high albedo, especially during or just after a snowfall event. As the snowfall amount can be obtained from an atmospheric model, this simple scheme can also be used in other parameterizations.

    In CCSM3, the fraction of surface snow cover is expressed as H s/(H s+0.02), where H s is snow thickness in units of m, and the value of 0.02 was selected to reach good agreement with SHEBA data (Briegleb et al., 2004). To better reflect the influence of snow thickness, expressions should be modified to fit for the local horizontal surface of the Antarctic landfast sea ice. According to the local feature that the snow thickness during the campaign was thinner than SHEBA, and the fact that the 0.01-m thick snow cover can have a large impact on the albedo, we modified the expression to H s/(H s+0.01) (Table 1, row 3).

    As shown in Fig. 4, the modified parameterization shows much better agreement with observed albedos in 2010 and 2011. The mean standard deviation biases of the parameterized albedo were -0.00 0.03 and 0.01 0.05 in 2010 and 2011 (Table 2), respectively, relative to the observations. Furthermore, the parameterized albedo can effectively capture both the high and low albedo limits and the observed rapid drop-off in the melting period.

5. Conclusions
  • To understand the variations of Antarctic sea ice albedo and factors influencing its variations, and to evaluate the performance of current albedo parameterizations over Antarctic sea ice, we continuously measured the in-situ albedo over Antarctic coastal landfast sea ice in Prydz Bay, near Zhongshan Station. The observation periods covered austral spring and early summer of 2010 and 2011, which can be considered as representative of albedo evolution over Antarctic coastal landfast sea ice during the seasonal transition. The mean observed albedos in 2010 and 2011 were 0.70 and 0.49, respectively. The large difference of 0.21 was attributed to less snow accumulation in 2011. Synoptic events and cloud cover have a significant influence on albedo, leading to an average increase of 0.18 and 0.06 associated with snowfall and overcast skies, respectively.

    Using the observations, we examined the performance of different existing snow/sea ice albedo algorithms succeed in simulating variations in sea-ice albedo over the Antarctic coastal landfast sea ice. The four selected albedo schemes showed significantly different albedo evolutions from spring to early summer and, in general, the complex schemes considering snow thickness and the spectral distribution of incoming radiation produced a more reasonable albedo than the simple ones, particularly during the summer melting period. This is consistent with a previous study over Arctic sea ice (Liu et al., 2007). Both correlation and partial correlations showed that snow thickness is the most important factor in determining the albedo and should be added in albedo schemes. Considering two spectral bands, the CCSM3 parameterization showed the best result among all the parameterizations. However, it could not reflect an observed albedo higher than 0.80, nor capture the observed rapid drop-off during the melting period, and highly overestimated the bare ice and melting albedo in 2011.

    Based on the observational analysis in this study, a modified parameterization was developed based on the CCSM3 parameterization. By further considering the effects of synoptic events and cloud cover, as well as the local landfast sea-ice surface characteristics, the modified parameterization effectively captured the observed variations in the albedo. As the snowfall information can be obtained from weather or climate models, this parameterization can be easily applied. However, it should be noted that further evaluations with more field observations over Antarctic sea ice are still strongly recommended.

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