Antarctic Sea Ice Processes And Climate Research Articles

Comparison of sea ice classification methods based on satellite scatterometer and radiometer data in the Weddell Sea, Antarctica

AbstractInformation on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.

Interannual variability in sea-ice thickness in the pack-ice zone off Lützow–Holm Bay, East Antarctica

Under the Japanese Antarctic Research Expedition (JARE) program, sea-ice thickness has been routinely monitored off Lützow–Holm Bay (East Antarctica) during the summer (mid-December to early January) since 2000/01, using an electromagnetic induction (EM) instrument onboard the icebreaker Shirase. Analysis of these data over a 10-year period, combined with visual observations using a simplified form of the ASPeCt (Antarctic Sea ice Processes and Climate) protocol, suggests a strong interannual variability in sea-ice thickness in this region. For the repeat pack-ice observation area, where the sea-ice thickness averaged over the nine seasons is ∼1.9 m, mean thicknesses of observed sea-ice in 2010/11 and 2011/12 are exceptionally large, at ∼3.3 and ∼5.8 m, respectively. This result is strongly related to regional patterns of sea ice dynamics. Ice convergence caused by anomalous northerly winds was particularly high in 2011/12, suggesting that the extremely thick ice observed in that season resulted largely from sea-ice deformation processes (including pressure ridging). Longer-term analysis of data from the past 34 years confirms that sea-ice conditions and thickness off Lützow–Holm Bay in summer are determined mainly by the large-scale pattern of atmospheric pressure in December.

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

Physical and morphological properties of sea ice in the Chukchi and Beaufort Seas during the 2010 and 2011 NASA ICESCAPE missions

Physical and morphological properties of sea ice in the Chukchi and western Beaufort seas were measured during the 2010 and 2011 June−July ICESCAPE (Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment) missions aboard the USCGC Healy. Observations of ice conditions, including ice thicknesses, types, and concentrations of primary, secondary, and tertiary categories were reported at 2-h intervals while the ship was in transit using the Antarctic Sea ice Processes and Climate (ASPeCt) protocol. On-ice surveys of ice thickness, melt-pond depth, and ice properties (including profiles of internal temperature, salinity, and isotopic composition) were conducted at 21 total ice stations (12 in 2010 and 9 in 2011). Comparison to historical ship-based observations confirms a multi-decadal transition from a multiyear to thinner, first year -dominated seasonal sea-ice pack in the region, with much earlier ice retreat than in past decades. The ice encountered was predominantly (>98%) first year, with un-deformed ice thicknesses ranging from 0.73 to 1.2m. Pond coverage was extensive, averaging 29% in 2010 and 19% in 2011, resulting in considerable light absorption in the ice and transmission of light to the ocean. Enhanced melting near the ice edge is consistent with transport of Pacific-origin heat and/or ice-albedo feedbacks. Sediment entrainment was visible in 7.5% and 10.9% of the ice in 2010 and 2011, respectively. Overall, the results indicate a regime shift in the characteristics of the ice cover has occurred in the region over recent decades, with substantive implications for thermodynamic forcing, light availability in the upper ocean, and biological and biogeochemical processes in the ice and water column beneath. The results presented have applications for the interpretation of optical and biological measurements in the Chukchi Sea and serve as record of ice conditions for assessing long-term change.

Comparison between AMSR-E ASI sea-ice concentration product, MODIS and pseudo-ship observations of the Antarctic sea-ice edge

AbstractThe performance of passive microwave sea-ice concentration products in the marginal ice zone and at the ice edge draws much attention in accuracy assessments. In this study, we generated 917 pseudo-ship observations from four Moderate Resolution Imaging Spectroradiometer (MODIS) images based on the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol to assess the quality of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI) concentrations at the ice edge in Antarctica. The results indicate that the ASI pixels in the pseudo-ASPeCt observations have a mean ice concentration of 13% and are significantly different from the well-established 15% threshold. The average distance between the pseudo-ice edge and the 15% threshold contour is ~10 km. The correlation between the sea-ice concentration (SIC), SICASI and SICMODIS values at the ice edge was considerably lower than the high coefficients obtained from a transect analysis. Underestimation of SICASI occurred in summer, whereas no clear bias was observed in winter. The proposed method provides an opportunity to generate a new source of reference data in which the spatial coverage is wider and more flexible than in traditional in situ observations.

Summer Antarctic sea ice as seen by ASAR and AMSR-E and observed during two IPY field cruises: a case study

AbstractEnvisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath Mode (WSM) images are used to derive C-band HH-polarization normalized radar cross sections (NRCS). These are compared with ice-core analysis and visual ship-based observations of snow and ice properties observed according to the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol during two International Polar Year summer cruises (Oden 2008 and Palmer 2009) in West Antarctica. Thick first-year (TFY) and multi-year (MY) ice were the dominant ice types. the NRCS value ranges between –16.3 ± 1.1 and –7.6 ± 1.0 dB for TFY ice, and is –12.6 ± 1.3 dB for MY ice; for TFY ice, NRCS values increase from –~15 dB to –9 dB from December/January to mid-February. In situ and ASPeCt observations are not, however, detailed enough to interpret the observed NRCS change over time. Co-located Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) vertically polarized 37 GHz brightness temperatures (TB37V), 7 day and 1 day averages as well as the TB37V difference between ascending and descending AMSR-E overpasses suggest the low NRCS values (–15 dB) are associated with snowmelt being still in progress, while the change towards higher NRCS values (–9 dB) is caused by commencement of melt– refreeze cycles after about mid-January.

Intercomparisons of Antarctic sea ice types from visual ship, RADARSAT-1 SAR, Envisat ASAR, QuikSCAT, and AMSR-E satellite observations in the Bellingshausen Sea

Antarctic Sea Ice Processes and Climate (ASPeCt) visual ship-based observations were conducted in the Bellingshausen Sea during the Sea Ice Mass Balance in the Antarctic (SIMBA) cruise in austral spring 2007. A total of 59 ASPeCt observations are compared to coincident satellite active and passive microwave data. Envisat and RADARSAT-1 C-Band HH-polarization radar backscatter values (called NRCS henceforth) are derived on km-scales for six individual ice types and ice type mixtures. C-Band HH-polarized and Ku-Band VV-polarized NRCS are extracted on several 10 km-scale areas from coincident Envisat, RADARSAT-1, and QuikSCAT radar images for areas primarily covered with multiyear, deformed first-year, and undeformed young ice, as well as ice of the marginal ice zone (MIZ). The C-Band NRCS permits distinction between first-year, MIZ, and undeformed young ice. However, NRCS of the multiyear ice zone overlaps with that of the other ice zones and types. Ku-Band NRCS obtained for the same ice types permits discrimination of the first-year ice zone only. Obtained NRCS agree with those of previous studies and suggest a high degree of deformation and considerable potential for flooding for the first-year ice case. In comparison to large scale NRCS, AMSR-E snow depth values form two clearly separated clusters, one for 0.24–0.35 m depth (first-year ice zone) and one for 0.36–0.54 m depth (multiyear ice zone). However, a comparison to ASPeCt observations suggests a remarkable underestimation of the snow depth by AMSR-E in the multiyear–first-year-ice transition zone and for first-year cake ice. Nevertheless, a fusion of the coarse AMSR-E snow depth ranges for interior pack ice regions with radar imagery at large scale, appears promising for mapping the major zones (MIZ and Pack Ice) and ice types (first-year and multiyear) of Antarctic sea ice on a circumpolar basis.

EISCAM — Digital image acquisition and processing for sea ice parameters from ships

Abstract Human observations and records of sea ice conditions from ships of opportunity in polar regions have been formalized under ASPeCt (Antarctic Sea Ice Processes and Climate) protocols as part of the Scientific Committee on Antarctic Research (SCAR) initiative, Global Change in Antarctica. Human observations, however, are subject to both bias and level of expertise. In support of ASPeCt observations, a ship-based, ice condition imagery acquisition, processing and analysis system is developed and presented in this paper. Digital images captured from recorded oblique-view video of sea ice during the inbound and outbound transits of the icebreaker NB Palmer, during the 2007 SIMBA (Sea Ice Mass Balance in Antarctic) cruise, were used to demonstrate the method. The resultant orthorectified and spatially registered images can thus be used to derive ice concentration, ice types, floe sizes, and area of deformed ice, parameters recorded by an ASPeCt observer based on experience. An assessment of total ice concentration, utilizing a digital number threshold method, for a 30 image sequence spanning 10 min, was compared to the corresponding ASPeCt observation and found good agreement. A k-means unsupervised classification technique was employed to evaluate the spectral separability of pixels associated with differing ice types. A significant source of visual perspective bias was discovered in associated with uneven spatial distribution of open-water leads. Convolution spatial filtering was employed to assess area of deformed ice during night-time conditions, when ship's floodlights illuminate the ice field. Transformation of individual image elements, such as floes, to vector-based polygons enabled accurate measurement of dimension, thus providing a frequency distribution of floe sizes, currently not possible with existing ASPeCt protocols. Limitations of this acquisition and analysis technique, future improvements, and coordination with the ASPeCt program were discussed.

Thickness distribution of Antarctic sea ice

Ship‐based observations are used to describe regional and seasonal changes in the thickness distribution and characteristics of sea ice and snow cover thickness around Antarctica. The data set comprises 23,373 observations collected over more than 2 decades of activity and has been compiled as part of the Scientific Committee on Antarctic Research (SCAR) Antarctic Sea Ice Processes and Climate (ASPeCt) program. The results show the seasonal progression of the ice thickness distribution for six regions around the continent together with statistics on the mean thickness, surface ridging, snow cover, and local variability for each region and season. A simple ridge model is used to calculate the total ice thickness from the observations of level ice and surface topography, to provide a best estimate of the total ice mass, including the ridged component. The long‐term mean and standard deviation of total sea ice thickness (including ridges) is reported as 0.87 ± 0.91 m, which is 40% greater than the mean level ice thickness of 0.62 m. Analysis of the structure function along north/south and east/west transects revealed lag distances over which sea ice thickness decorrelates to be of the order of 100–300 km, which we use as a basis for presenting near‐continuous maps of sea ice and snow cover thickness plotted on a 2.5° × 5.0° grid.

Antarctic sea ice elevation from satellite radar altimetry

In situ measurements of sea ice thickness from ship and upward‐looking sonar are used to assess the potential for satellite radar altimetry to provide information on Antarctic sea ice thickness. A climatology of satellite ice elevation estimates is compared to an Antarctic sea ice thickness climatology made from the Antarctic Sea Ice Processes and Climate (ASPeCt) data set. In addition monthly, regional, satellite ice elevation estimates are compared to ULS ice draft data. The results show reasonable spatial agreement between the satellite and in‐situ data, and show regional signals of change in ice elevation in line with that which would be expected. The results show some promise for providing information on Antarctic ice thickness from radar altimetry missions such as CryoSat. However, further studies into snow and ice density and the radar penetration into the Antarctic snow cover are required.

Surface Albedo of the Antarctic Sea Ice Zone

AbstractIn three ship-based field experiments, spectral albedos were measured at ultraviolet, visible, and near-infrared wavelengths for open water, grease ice, nilas, young “grey” 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 “ice-only” 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.

Utilizing the ASPeCt sea ice thickness data set to evaluate a global coupled sea ice–ocean model

Simulated sea ice thickness in the ORCA2‐LIM coupled sea ice–ocean model is compared with thicknesses from the Antarctic Sea Ice Processes and Climate (ASPeCt) database. We find a qualitative agreement of the large‐scale patterns of ice thickness distribution. Regional averages for the various sectors of the Southern Ocean yield a very good correspondence between observations and model data. Exceptions are the eastern Bellingshausen and northwestern Weddell Seas. A poor representation of the Antarctic Peninsula in the atmospheric forcing data and the related overestimation of westerly winds in this region lead to a spurious accumulation of sea ice on the western side of the peninsula and to an underestimation of sea ice coverage on the eastern side. Since the spatial scale of observations is not comparable to the size of a model grid cell, there is little agreement between individual observations and the corresponding model ice thicknesses. A model analysis of the seasonal and interannual variability indicates that the ASPeCt data underestimate the climatological ice thickness in the central and southern Weddell Sea and the eastern Ross Sea by up to 1 m. Because of a winter bias in the observations an overestimation of similar magnitude is expected in the Bellingshausen Sea. Ice thickness data in most of the Indo‐Pacific sector appear to be representative for the long‐term climatology. A model estimate of the bias is used to compute a revised distribution of climatological sea ice thickness.

Estimating the thickness of ridged sea ice from ship observations in the Ross Sea

Ship-based observations of sea ice thickness using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol provide information on ice thickness distribution at relatively low cost. This protocol uses a simple formula to calculate the mass of ice in ridges based on surface observations. We present two new formulae and compare these with results from the “Original” formula using data obtained in the Ross Sea in autumn and winter. The new “r-star” formula uses a more realistic ratio of sail and keel areas to transform dimensions of sails to estimates of mean keel areas. As a result, estimates of “equivalent thickness” (i.e. mean thickness of ice in ridged areas) increased by over 200%. The new “Probability” formula goes one step further, by incorporating the probability that a sail is associated with a keel underwater, and the probability that keels may be found under level surfaces. This resulted in estimates of equivalent thickness comparable with the Original formula. Estimates of equivalent thickness at one or two degree latitude resolution are sufficiently accurate for validating sea ice models. Although ridges are small features in the Ross Sea, we have shown that they constitute a significant fraction of the total ice mass.