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: