“…Snow and sea ice are fully coupled with respect to the heat conduction and snow-to-ice transformation. The model has been used to simulate the evolution of snow and sea-ice thickness in the Arctic others, 2008, 2014;Yang and others, 2015;Merkouriadi and others, 2017) and Antarctic (Vihma and others, 2002;Zhao and others, 2017;2019a, 2019b.…”
Section: Hightsi Model and Fips Setupmentioning
confidence: 99%
“…Fast ice is a navigational obstacle, particularly for ships that need to be docked in the harbors of Antarctic stations. The physical factors affecting the annual cycle of fast ice thickness in Prydz Bay include the incoming and outgoing solar shortwave and thermal longwave radiative fluxes; turbulent surface fluxes of sensible and latent heat; density, thermal conductivity and depth of snow; as well as oceanic heat flux at the ice base (Heil and others, 1996; Lei and others, 2010; Yang and others, 2015; Zhao and others, 2019a). Among those factors, the atmospheric forcing plays a dominant role in land-ice mass balance (Heil, 2006).…”
Section: Introductionmentioning
confidence: 99%
“…Close to the coast, the fast ice may entirely melt away for a couple of weeks during summer. The fast ice in Prydz Bay can grow up to 1.2–1.8 m for the first-year ice (FYI) and 2.0–3.0 m for second or multi-year ice (SYI/MYI) by the end of winter (Lei and others, 2010; Zhao and others, 2019a).…”
AbstractA Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.
“…Snow and sea ice are fully coupled with respect to the heat conduction and snow-to-ice transformation. The model has been used to simulate the evolution of snow and sea-ice thickness in the Arctic others, 2008, 2014;Yang and others, 2015;Merkouriadi and others, 2017) and Antarctic (Vihma and others, 2002;Zhao and others, 2017;2019a, 2019b.…”
Section: Hightsi Model and Fips Setupmentioning
confidence: 99%
“…Fast ice is a navigational obstacle, particularly for ships that need to be docked in the harbors of Antarctic stations. The physical factors affecting the annual cycle of fast ice thickness in Prydz Bay include the incoming and outgoing solar shortwave and thermal longwave radiative fluxes; turbulent surface fluxes of sensible and latent heat; density, thermal conductivity and depth of snow; as well as oceanic heat flux at the ice base (Heil and others, 1996; Lei and others, 2010; Yang and others, 2015; Zhao and others, 2019a). Among those factors, the atmospheric forcing plays a dominant role in land-ice mass balance (Heil, 2006).…”
Section: Introductionmentioning
confidence: 99%
“…Close to the coast, the fast ice may entirely melt away for a couple of weeks during summer. The fast ice in Prydz Bay can grow up to 1.2–1.8 m for the first-year ice (FYI) and 2.0–3.0 m for second or multi-year ice (SYI/MYI) by the end of winter (Lei and others, 2010; Zhao and others, 2019a).…”
AbstractA Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.
“…Lei and others (2010) found the mean snow thickness was close to 2.0 cm, but for some years the snow thickness is much larger, e.g. Zhao and others (2019) observed a mean snow thickness of 11.3 cm. Fig.…”
Section: Observationsmentioning
confidence: 98%
“…The fast-ice observation site is located in the coastal area off Zhongshan Station (69°22 ′ S, 76°22 ′ E), Prydz Bay, East Antarctica (Yang and others, 2016;Zhao and others, 2019). Prydz Bay is mostly covered by seasonal sea ice.…”
The surface spectral albedo was measured over coastal landfast sea ice in Prydz Bay (off Zhongshan Station), East Antarctica from 5 October to 26 November of 2016. The mean albedo decreased from late-spring to early-summer, mainly responding to the change in surface conditions from dry (phase I) to wet (phase II). The evolution of the albedo was strongly influenced by the surface conditions, with alternation of frequent snowfall events and katabatic wind that induce snow blowing at the surface. The two phases and day-to-day albedo variability were more pronounced in the near-infrared albedo wavelengths than in the visible ones, as the near-infrared photons are more sensitive to snow metamorphism, and to changes in the uppermost millimeters and water content of the surface. The albedo diurnal cycle during clear sky conditions was asymmetric with respect to noon, decreasing from morning to evening over full and patchy snow cover, and decreasing more rapidly in the morning over bare ice. We conclude that snow and ice metamorphism and surface melting dominated over the solar elevation angle dependency in shaping the albedo evolution. However, we realize that more detailed surface observations are needed to clarify and quantify the role of the various surface processes.
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