Modelling snow and ice thickness in the coastal Kara Sea, Russian Arctic

AbstractSnow and ice thickness in the coastal Kara Sea, Russian Arctic, were investigated by applying the thermodynamic sea-ice model HIGHTSI. The external forcing was based on two numerical weather prediction (NWP) models: the High Resolution Limited Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. A number of model experiments were carried out applying different snow parameterization schemes. The modelled ice thickness was compared with in situ measurements and the modelled snow thickness was compared with the NASA Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow thickness. The HIRLAM and ECMWF model results agreed with each other on air temperature and wind. The NWP model precipitation forecasts caught up the synoptic-scale snowfall events, but the magnitude was liable to errors. The ice growth was modelled reasonably well applying HIGHTSI either with a simple parameterization for snow thickness or with the HIRLAM or ECMWF model precipitation as input. For the latter, however, an adjustment of snow accumulation in early winter was necessary to avoid excessive accumulation and consequent underestimation of ice thickness. Applying effective snow heat conductivity improved the modelled ice thickness. The HIGHTSI-modelled snow thickness had a seasonal evolution similar to that of the AMSR-E snow thickness. New field data are urgently needed to validate NWP and ice models and remote-sensing products for snow and sea ice in the Kara Sea.

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

Annals of Glaciology ◽

10.3189/2013aog62a157 ◽

2013 ◽

Vol 54 (62) ◽

pp. 261-266 ◽

Cited By ~ 17

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.

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Snow-depth observations by adventurers traveling on Arctic sea ice

Annals of Glaciology ◽

10.3189/172756411795931552 ◽

2011 ◽

Vol 52 (57) ◽

pp. 369-376 ◽

Cited By ~ 12

Author(s):

Sebastian Gerland ◽

Christian Haas

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Basin ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Snow Thickness ◽

Snow And Ice

AbstractSnow depth is a key parameter for assessing the sea-ice mass budget in the Arctic and for the surface energy balance at the atmosphere–snow–ice–ocean interfaces. However, scientific expeditions to the high Arctic Ocean are rare, and for large parts of the year no snow and ice data are collected in situ in most regions. Therefore any additional in situ observations of snow depth are of interest to the scientific community. Arctic adventurers and tourists are among the most frequent visitors to the Arctic Ocean and North Pole. If properly trained and carefully adhering to standard protocols, they could collect valuable snow-depth data from large regions. Here we analyse such data from four Arctic basin ski traverses carried out between 1994 and 2007. Individual datasets show characteristic regional differences of snow thickness, which provide invaluable information for the validation of models and satellite data. the observations were made applying a continuously upgraded observation guideline scheme. Earlier observations were based on a relatively broad view of easily observable snow and ice parameters. Improvements included requirements of more detailed snow-thickness surveys in order to observe the spatial variability over varying sea-ice surfaces in a better way. Possibilities for, and limitations of, ice-thickness estimates and measurements are also discussed. Often, assessment of ice thickness is more problematic since measurements are either time-consuming or biased, meaning that possibilities for collecting large datasets are limited.

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

10.5194/tc-2020-168 ◽

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.

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Arctic sea-ice albedo derived from RGPS-based ice-thickness estimates

Annals of Glaciology ◽

10.3189/172756401781818103 ◽

2001 ◽

Vol 33 ◽

pp. 225-229 ◽

Cited By ~ 8

Author(s):

R.W. Lindsay

Keyword(s):

Sea Ice ◽

Empirical Relationship ◽

Early Spring ◽

Snow Accumulation ◽

Arctic Sea Ice ◽

Ice Thickness ◽

First Year ◽

Large Set ◽

Radar Images ◽

Arctic Sea

AbstractThe RADARSAT geophysical processor system (RGPS) uses sequential synthetic aperture radar images of Arctic sea ice taken every 3 days to track a large set of Lagrangian points over the winter and spring seasons. The points are the vertices of cells, which are initially square and 10 km on a side, and the changes in the area of these cells due to opening and closing of the ice are used to estimate the fractional area of a set of first-year ice categories. The thickness of each category is estimated by the RGPS from an empirical relationship between ice thickness and the freezing degree-days since the formation of the ice. With a parameterization of the albedo based on the ice thickness, the albedo may be estimated from the first-year ice distribution. We compute the albedo for the first spring processed by the RGPS, the early spring of 1997. The data include most of the Beaufort and Chukchi Seas. We find that the mean albedo is 0.79 with a standard deviation of 0.04, with lower albedo values near the edge of the perennial ice zone. The biggest source of error is likely the assumed rate of snow accumulation on new ice.

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Fast Ice Prediction System (FIPS) for land-fast sea ice at Prydz Bay, East Antarctica: an operational service for CHINARE

Annals of Glaciology ◽

10.1017/aog.2020.46 ◽

2020 ◽

pp. 1-13

Author(s):

Jiechen Zhao ◽

Bin Cheng ◽

Timo Vihma ◽

Petra Heil ◽

Fengming Hui ◽

...

Keyword(s):

Sea Ice ◽

Ice Thickness ◽

Cold Bias ◽

Prediction System ◽

Atmospheric Forcing ◽

Ice Melt ◽

Fast Ice ◽

Sar Imagery ◽

Operational Service ◽

Snow And Ice

Abstract A 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.

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Winter Arctic sea ice bottom evolution detected by thermistor string-based ice mass balance buoys (SIMBA)

10.5194/egusphere-egu2020-22171 ◽

2020 ◽

Author(s):

Bin Cheng ◽

Timo Vihma ◽

Zeling Liao ◽

Ruibo Lei ◽

Mario Hoppmann ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Mass Balance ◽

Arctic Sea Ice ◽

Heating Element ◽

The Arctic ◽

Ice Thickness ◽

The Arctic Ocean ◽

Ice Mass Balance ◽

Snow And Ice

A thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) has been developed in recent years and used for monitoring snow and ice mass balance in the Arctic Ocean. SIMBA measures vertical environment temperature (ET) profiles through the air-snow-sea ice-ocean column using a thermistor string (5 m long, sensor spacing 2cm). Each thermistor sensor equipped with a small identical heating element. A small voltage was applied to the heating element so that the heat energy liberated in the vicinity of each sensor is the same. The heating time intervals lasted 60 s and 120 s, respectively. The heating temperatures (HT) after these two intervals were recorded. The ET was measured 4 times a day and once per day for the HT.A total 15 SIMBA buoys have been deployed in the Arctic Ocean during the Chinese National Arctic Research Expedition (CHINARE) 2018 and the Nansen and Amundsen Basins Observational System (NABOS) 2018 field expeditions in late autumn. We applied a recently developed SIMBA algorithm to retrieve snow and ice thickness using SIMBA ET and HT temperature data. We focus particularly on sea ice bottom evolution during Arctic winter.In mid-September 2018, 5 SIMBA buoys were deployed in the East Siberian Sea (NABOS2018) where snow was in practical zero cm and ice thickness ranged between 1.8 m &#8211; 2.6 m. By the end of May, those SIMBA buoys were drifted in the central Arctic where snow and ice thicknesses were around 0.05m - 0.2m and 2.6m &#8211; 3.2m, respectively. For those 10 SIMBA buoys deployed by the CHINARE2018 in the Chukchi Sea and Canadian Basin, the initial snow and ice thickness were ranged between 0.05m &#8211; 0.1cm and 1.5m &#8211; 2.5m, respectively. &#160;By the end of May, those SIMBA buoys were drifted toward the north of Greenland where snow and ice thicknesses were around 0.2m - 0.3m and 2.0m &#8211; 3.5m, respectively. The ice bottom evolution derived by SIMBA algorithm agrees well with SIMBA HT identified ice-ocean interfaces. We also perform a preliminary investigation of sea ice bottom evolution measured by several SIMBA buoys deployed during the MOSAiC leg1 field campaign in winter 2019/2020.&#160;&#160;

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Taking a look at both sides of the ice: comparison of ice thickness and drift speed as observed from moored, airborne and shore-based instruments near Barrow, Alaska

Annals of Glaciology ◽

10.3189/2015aog69a565 ◽

2015 ◽

Vol 56 (69) ◽

pp. 363-372 ◽

Cited By ~ 18

Author(s):

Andrew R. Mahoney ◽

Hajo Eicken ◽

Yasushi Fukamachi ◽

Kay I. Ohshima ◽

Daisuke Simizu ◽

...

Keyword(s):

Sea Ice ◽

Probability Distributions ◽

Median Filter ◽

Acoustic Doppler Current Profiler ◽

Ice Thickness ◽

Current Profiler ◽

Airborne Electromagnetic ◽

Airborne Electromagnetic Survey ◽

Derived Data ◽

Snow Thickness

AbstractData from the Seasonal Ice Zone Observing Network (SIZONet) acquired near Barrow, Alaska, during the 2009/10 ice season allow novel comparisons between measurements of ice thickness and velocity. An airborne electromagnetic survey that passed over a moored Ice Profiling Sonar (IPS) provided coincident independent measurements of total ice and snow thickness and ice draft at a scale of 10 km. Once differences in sampling footprint size are accounted for, we reconcile the respective probability distributions and estimate the thickness of level sea ice at 1.48 ± 0.1 m, with a snow depth of 0.12 ± 0.07 m. We also complete what we believe is the first independent validation of radar-derived ice velocities by comparing measurements from a coastal radar with those from an under-ice acoustic Doppler current profiler (ADCP). After applying a median filter to reduce high-frequency scatter in the radar-derived data, we find good agreement with the ADCP bottom-tracked ice velocities. With increasing regulatory and operational needs for sea-ice data, including the number and thickness of pressure ridges, coordinated observing networks such as SIZONet can provide the means of reducing uncertainties inherent in individual datasets.

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Early winter ice and snow thickness distribution, ice structure and development of the western Ross Sea pack ice between the ice edge and the Ross Ice Shelf

Antarctic Science ◽

10.1017/s0954102097000242 ◽

1997 ◽

Vol 9 (2) ◽

pp. 188-200 ◽

Cited By ~ 50

Author(s):

Martin O. Jeffries ◽

Ute Adolphs

Keyword(s):

Sea Ice ◽

Layer Thickness ◽

Ross Sea ◽

Ice Thickness ◽

First Year ◽

Ross Ice Shelf ◽

Antarctic Sea Ice ◽

Pack Ice ◽

Ice Edge ◽

Snow And Ice

A study of early winter first-year sea ice conditions and development in the western Ross Sea in May and June 1995 included measurements of snow and ice thickness, freeboard, ice core structure and stable isotopic composition. These variables showed strong spatial variability between the Ross Ice Shelf and the ice edge 1400 km to the north, and indicate that the development of the Ross Sea pack ice is quite different from that observed in other Antarctic sea ice zones. The thinnest snow and ice occurred in a 200 km wide coastal zone. The thickest snow and ice were observed in a continental shelf zone 200–600 km from the coast where the average ice thickness (0.8 m) determined by drilling is as thick as first-year sea ice later in winter elsewhere in Antarctica. A zone of moderate snow and ice thickness occurred on the deep ocean from 600 km to the ice edge at 1400 km. Thermodynamic thickening of the ice in the inner pack ice, <800 km from the coast, was dominated by congelation ice growth, which occurred in a greater amount (65%) and in thicker layers (mean: 20 cm) than was observed in the outer pack ice >800 km from the coast (amount: 22%; mean layer thickness: 12 cm) and elsewhere in the Antarctic pack ice. The preponderance of congelation ice in the inner pack ice might be due to a low oceanic heat flux on the Ross Sea continental shelf, and a colder, less stormy environment which favours the more frequent and prolonged calm conditions necessary for significant congelation ice growth. In the outer pack ice, thermodynamic thickening occurred mainly by snow ice formation (mean layer thickness: 20 cm) while dynamic processes, i.e., rafting and ridging, caused the thickening of frazil ice and columnar ice (mean layer thickness: 14 cm and 12 cm respectively). A greater amount of snow ice (37%) occurred in the outer pack ice than in the inner pack ice (15%), and both values indicate that in the Ross Sea, unlike other Antarctic sea ice zones, there can be significant seawater flooding of the snow/ice interface and snow ice formation before midwinter.

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

10.5194/tc-2018-92 ◽

2018 ◽

Cited By ~ 1

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.

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

10.5194/egusphere-egu2020-1715 ◽

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

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 = &#8722;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&#8211;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&#8211;23% and 5&#8211;8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05&#8211;2 m, respectively.

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