Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

2020 ◽  
Author(s):  
Jiechen Zhao ◽  
Bin Cheng ◽  
Timo Vihma ◽  
Qinghua Yang ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Annual Cycle ◽  
Snow Depth ◽  
Snow Accumulation ◽  
East Antarctica ◽  
Prydz Bay ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Landfast Sea Ice

<p>The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = −0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53–0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4–23% and 5–8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05–2 m, respectively.</p>

scholarly journals Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

2006 ◽  
Vol 44 ◽  
pp. 281-287 ◽  
Author(s):  
Shotaro Uto ◽  
Haruhito Shimoda ◽  
Shuki Ushio
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Snow Depth ◽  
East Antarctica ◽  
Ice Thickness ◽  
First Year ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Landfast Ice ◽  
Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

scholarly journals Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

2018 ◽  
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Fast Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

scholarly journals Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

2019 ◽  
Vol 13 (4) ◽  
pp. 1409-1422
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack ◽  
Ethan Dale
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Mcmurdo Sound ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

scholarly journals Annual cycle of landfast sea ice in Prydz Bay, east Antarctica

2010 ◽  
Vol 115 (C2) ◽  
Author(s):  
Ruibo Lei ◽  
Zhijun Li ◽  
Bin Cheng ◽  
Zhanhai Zhang ◽  
Petra Heil
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
East Antarctica ◽  
Prydz Bay ◽  
Landfast Sea Ice

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

Research on Electromagnetic Inductive Measurement of Sea-Ice Thickness in Antarctic Prydz Bay

2008 ◽  
Vol 51 (2) ◽  
pp. 419-426 ◽  
Author(s):  
Jing-Xue GUO ◽  
Bo SUN ◽  
Gang TIAN ◽  
Bang-Bing WANG ◽  
Xiang-Pei ZHANG
Keyword(s):  
Sea Ice ◽  
Prydz Bay ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Inductive Measurement

scholarly journals Spatial variation of biogeochemical properties of landfast sea ice in the Gulf of Bothnia, Baltic Sea

2006 ◽  
Vol 44 ◽  
pp. 80-87 ◽  
Author(s):  
M. Steffens ◽  
M.A. Granskog ◽  
H. Kaartokallio ◽  
H. Kuosa ◽  
K. Luodekari ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Snow Depth ◽  
Large Scale ◽  
Spatial Scales ◽  
Thick Layer ◽  
Ice Cores ◽  
Ice Thickness ◽  
Gulf Of Bothnia ◽  
Landfast Sea Ice

AbstractHorizontal variation of landfast sea-ice properties was studied in the Gulf of Bothnia, Baltic Sea, during March 2004. In order to estimate their variability among and within different spatial levels, 72 ice cores were sampled on five spatial scales (with spacings of 10 cm, 2.5 m, 25 m, 250m and 2.5 km) using a hierarchical sampling design. Entire cores were melted, and bulk-ice salinity, concentrations of chlorophylla(Chla), phaeophytin (Phaeo), dissolved nitrate plus nitrite (DIN) as well as dissolved organic carbon (DOC) and nitrogen (DON) were determined. All sampling sites were covered by a 5.5–23 cm thick layer of snow. Ice thicknesses of cores varied from 26 to 58 cm, with bulk-ice salinities ranging between 0.2 and 0.7 as is typical for Baltic Sea ice. Observed values for Chla(range: 0.8–6.0 mg ChlaL–1; median: 2.9 mg ChlaL–1) and DOC (range: 37–397 μM; median: 95 μM) were comparable to values reported by previous sea-ice studies from the Baltic Sea. Analysis of variance among different spatial levels revealed significant differences on the 2.5km scale for ice thickness, DOC and Phaeo (with the latter two being positively correlated with ice thickness). For salinity and Chla, the 250 m scale was found to be the largest scale where significant differences could be detected, while snow depth only varied significantly on the 25 m scale. Variability on the 2.5 m scale contributed significantly to the total variation for ice thickness, salinity, Chlaand DIN. In the case of DON, none of the investigated levels exhibited variation that was significantly different from the considerable amount of variation found between replicate cores. Results from a principal component analysis suggest that ice thickness is one of the main elements structuring the investigated ice habitat on a large scale, while snow depth, nutrients and salinity seem to be of secondary importance.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Freeboard height and snow depth observed by floating GPS on land-fast sea ice in Nella Fjord, Antarctica

2020 ◽  
Vol 61 (82) ◽  
pp. 227-239
Author(s):  
Qingchuan Zhang ◽  
Fei Li ◽  
Jintao Lei ◽  
Shengkai Zhang ◽  
Zhuoming Ding ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Single Point ◽  
Pressure Gauge ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Zhongshan Station ◽  
Ocean Topography ◽  
Bottom Pressure Gauge ◽  
The Relationship

AbstractAlthough altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

Sign in / Sign up

Export Citation Format

Share Document