scholarly journals A New Apparatus for Monitoring Sea Ice Thickness Based on the Magnetostrictive-Delay-Line Principle

2009 ◽  
Vol 26 (4) ◽  
pp. 818-827 ◽  
Author(s):  
Ruibo Lei ◽  
Zhijun Li ◽  
Yanfeng Cheng ◽  
Xin Wang ◽  
Yao Chen
Keyword(s):  
Sea Ice ◽  
Delay Line ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Zhongshan Station ◽  
Mdl Principle ◽  
Valid Data ◽  
New Apparatus ◽  
Autumn And Winter ◽  
Snow Thickness

Abstract High-precision ice thickness observations are required to gain a better understanding of ocean–ice–atmosphere interactions and to validate numerical sea ice models. A new apparatus for monitoring sea ice and snow thickness has been developed, based on the magnetostrictive-delay-line (MDL) principle for positioning sensors. This system is suited for monitoring fixed measurement sites on undeformed ice. The apparatus presented herein has been tested on landfast ice near Zhongshan Station, East Antarctica, for about 6 months during the austral autumn and winter of 2006; valid data records from the deployment are available for more than 90% of the deployment’s duration. The apparatus’s precision has been estimated to be ±0.002 m for the deployment. Therefore, it is possible that this apparatus may become a standard for sea ice/snow thickness monitoring.

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.

scholarly journals Small-scale horizontal variability of snow, sea-ice thickness and freeboard in the first-year ice region north of Svalbard

2013 ◽  
Vol 54 (62) ◽  
pp. 261-266 ◽  
Author(s):  
Jari Haapala ◽  
Mikko Lensu ◽  
Marie Dumont ◽  
Angelika H.H. Renner ◽  
Mats A. Granskog ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Sea Ice ◽  
Snow Accumulation ◽  
Small Scale ◽  
Ice Thickness ◽  
First Year ◽  
Sea Ice Thickness ◽  
The Mean ◽  
Ridged Ice ◽  
Snow Thickness

AbstractVariability of sea-ice and snow conditions on the scale of a few hundred meters is examined using in situ measurements collected in first-year pack ice in the European Arctic north of Svalbard. Snow thickness and surface elevation measurements were performed in the standard manner using a snow stick and a rotating laser. Altogether, 4109 m of measurement lines were surveyed. The snow loading was large, and in many locations the ice freeboard was negative (38.8% of snowline measurements), although the modal ice and snow thickness was 1.8 m. The mean of all the snow thickness measurements was 36 cm, with a standard deviation of 26 cm. The mean freeboard was only 3 cm, with a standard deviation of 23 cm. There were noticeable differences in snow thickness among the measurement sites. Over the undeformed ice areas, the mean snow thickness and freeboard were 23 and 2.4 cm, respectively. Over the ridged ice areas, the mean freeboard was only –0.3 cm due to snow accumulation on the sails of ridges (average thickness 54 cm). These findings imply that retrieval algorithms for converting freeboard to ice thickness should take account of spatial variability of snow cover.

scholarly journals Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2

2018 ◽  
Vol 12 (8) ◽  
pp. 2789-2801 ◽  
Author(s):  
Ron Kwok ◽  
Sahra Kacimi
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Basal Layer ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Mean Differences ◽  
The Impact ◽  
Snow Thickness

Abstract. We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.40±1.07 (2011) to 2.60±1.15 m (2014) and snow depth from 35.8±11.5 (2016) to 43.6±10.2 cm (2014), suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. The impact of undersampling the thin end of the snow depth distribution on the regional statistics, due to the resolution of the snow radar, is assessed. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2) sampled along the same transect are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow–ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thicknesses computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore generally higher; mean differences in sea ice thickness along a transect are up to ∼0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, with which the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding the limits of retrievals of Weddell Sea ice thickness from satellite altimeters.

scholarly journals Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2

2018 ◽  
Author(s):  
Ron Kwok ◽  
Sahra Kacimi
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Basal Layer ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Radar Scattering ◽  
Mean Differences ◽  
Snow Thickness

Abstract. We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.4 ± 1.07 (2011) to 2.60 ± 1.15 m (2014), and snow depth from 30.0 ± 8.51 (2016) to 43.6 ± 10.2 cm (2014); suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2), sampled along the same transect, are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow-ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thickness computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore general higher; mean differences in sea ice thickness along a transect are up to ~ 0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice sea thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, where the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding limits of retrievals of Weddell Sea ice thickness from satellite altimeters.

Improving Arctic Sea Ice Prediction Though Freeboard Assimilation

2021 ◽  
Author(s):  
Imke Sievers ◽  
Till Rasmussen ◽  
Lars Stenseng
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Elevation ◽  
Ice Formation ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Snow Thickness

<p>With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood. <br>One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error. <br>To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.</p>

scholarly journals Variability of fast-ice thickness in Spitsbergen fjords

2006 ◽  
Vol 44 ◽  
pp. 231-239 ◽  
Author(s):  
Sebastian Gerland ◽  
Richard Hall
Keyword(s):  
Sea Ice ◽  
Early Spring ◽  
Ice Thickness ◽  
Western Coast ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Svalbard Archipelago ◽  
Average Maximum ◽  
Before And After ◽  
Snow Thickness

AbstractDetailed measurements of Sea-ice thickness and Snow on Sea ice were recorded at different locations in fjords along the western coast of Spitsbergen, the largest island in the Svalbard archipelago, in 2004. Data corresponding to the ice Situation before and after melt onset were collected for Kongsfjorden and Van Mijenfjorden, while Hornsund was investigated once during early Spring. Profiles of total thickness (snow plus ice thickness) were measured, together with Some Snow-thickness measurements. Total thicknesses were measured with a portable electromagnetic instrument and at Selected Sites by drilling. The three fjords Show Some differences in measured thicknesses, connected to individual conditions. However, total thickness does not differ Substantially between the three fjords before melt onset. The modal total thickness for all three fjords before melt onset was 1.075 m, and the corresponding modal Snow thickness was 0.225 m (bin width 0.05 m). Long-term Kongsfjorden ice-thickness data Since 1997 Show that the maximum ice thickness varies Significantly interannually, as observed at other Arctic Sites. The average maximum ice thickness for Kongsfjorden was 0.71 m (years 1997–98, 2000 and 2002–05), and the respective average maximum Snow thickness was 0.22 m. In Kongsfjorden, 2004 was the year with highest maximum total thickness and Snow thickness relative to the other years.

scholarly journals The sensitivity of landfast sea ice to atmospheric forcing in single-column model simulations: a case study at Zhongshan Station, Antarctica

2021 ◽  
Author(s):  
Fengguan Gu ◽  
Qinghua Yang ◽  
Frank Kauker ◽  
Changwei Liu ◽  
Guanghua Hao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Zhongshan Station ◽  
Single Column ◽  
In Situ Observations

Abstract. Single-column sea ice models are used to focus on the thermodynamic evolution of the ice. Generally these models are forced by atmospheric reanalysis in absence of atmospheric in situ observations. Here we assess the sea ice thickness (SIT) simulated by a single-column model (ICEPACK) with in situ observations obtained off Zhongshan Station for the austral winter of 2016. In the reanalysis the surface air temperature is about 1 °C lower, the total precipitation is about 2 mm day−1 larger, and the surface wind speed is about 2 m s−1 higher compared to the in situ observations, respectively. Using sensitivity experiments we evaluate the simulation bias in sea ice thickness due to the uncertainty in the individual atmospheric forcing variables. We show that the unrealistic precipitation in the reanalysis leads to a bias of 14.5 cm in sea ice thickness and of 17.3 cm in snow depth. In addition, our data show that increasing snow depth works to gradually inhibits the growth of sea ice associated with thermal blanketing by the snow due to changing the vertical heat flux. Conversely, given suitable conditions, the sea ice thickness may grow suddenly when the snow load gives rise to flooding and leads to snow-ice formation. A potential mechanism to explain the different characteristics of the precipitation bias on snow and sea ice is discussed. The flooding process for landfast sea ice might cause different effect compared to pack ice, thus need to be reconsidered in ICEPACK. Meanwhile, the overestimation in surface wind speed in reanalysis is likely responsible for the underestimation in simulated snow depth, however this had little influence on the modelled ice thickness.

scholarly journals Implications of surface flooding on airborne thickness measurements of snow on sea ice

2020 ◽  
Author(s):  
Anja Rösel ◽  
Sinead Louise Farrell ◽  
Vishnu Nandan ◽  
Jaqueline Richter-Menge ◽  
Gunnar Spreen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Validation Studies ◽  
The Arctic ◽  
Airborne Radar ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Thickness Measurements ◽  
Snow Thickness ◽  
Snow And Ice

Abstract. Snow thickness observations from airborne snow radars, such as the NASA’s Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow thickness measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime, caused by deep snow on thin sea ice. During the Norwegian young sea ICE expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on March 19, 2015, during which ground truth data were collected during an OIB overflight. Snow and ice thickness measurements were obtained across a two dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snow pack were observed during the N-ICE2015 expedition, due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking and saturation into the basal snow layers, causing more diffuse scattering and influenced the airborne radar signal to detect the radar main scattering horizon well above the snow/sea ice interface, resulting in a subsequent underestimation of total snow thickness, if only radar-based information is used. The average airborne snow thickness was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow thickness, a 0.06 m underestimate in snow thickness by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow thickness distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that, with frequent flooding of the snow-ice interface in specific regions of the Arctic in the future, it may result in an underestimate of snow thickness or an overestimate of ice freeboard, measured from radar altimetry, thereby affecting the accuracy of sea ice thickness estimates.

Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project

2021 ◽  
Author(s):  
Michel Tsamados ◽  
Keyword(s):  
Sea Ice ◽  
Primary Objective ◽  
The Arctic ◽  
Ice Thickness ◽  
Laser Altimeter ◽  
Sea Ice Thickness ◽  
Model Studies ◽  
Polar Snow ◽  
The Impact ◽  
Snow Thickness

<p>Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent ‘golden era in polar altimetry’ with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent ‘operational‘ period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.</p><p> </p>

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