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

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.

Download Full-text

Comparing methods of measuring sea-ice density in the East Antarctic

Annals of Glaciology ◽

10.3189/2015aog69a814 ◽

2015 ◽

Vol 56 (69) ◽

pp. 77-82 ◽

Cited By ~ 9

Author(s):

Jennifer K. Hutchings ◽

Petra Heil ◽

Oliver Lecomte ◽

Roger Stevens ◽

Adam Steer ◽

...

Keyword(s):

Sea Ice ◽

Error Estimates ◽

East Antarctica ◽

Ice Thickness ◽

First Year ◽

Density Estimates ◽

Sea Ice Thickness ◽

The Mean ◽

Volume Method ◽

Mass Volume

AbstractRemotely sensed derivation of sea-ice thickness requires sea·ice density. Sea-ice density was estimated with three techniques during the second Sea Ice Physics and Ecosystem eXperimett (SIPEX-II, September-November 2012, East Antarctica). The sea ice was first-year highly deformed, mean thicknsss 1.2 m with layers, consistent with rafting, and 6-7/10 columnar ice and 3/10 granular ice. Ice density was found to be lower than values (900-920 kg m−3 used previously to derive ice thickness,, with columnar ice mean density of 870 kg m− 3. At two different ice stations the mean density of the ice was 800 kg m–3, the lower density reflecting a high percentage of porous granular ice at the second station. Error estimates for mass/volume and liquid/solid water methods are presented. With 0.1 m long, 0.1 m core samples, the error on individual density estimates is 28 kg m-3. Errors are larger for smaller machined blocks. Errors increase to 46 kg m-3 if the liquid/solid volume method is used. The mass/vouume method has a low bias due to brine drainage of at least 5%. Bulk densities estimated from ice and snow measurements along 100 m transects were high, and likely unrealistic as the assumption of isostatcc balance is not suitable over these length scales in deformed ice.

Download Full-text

Interannual sea ice thickness variability in the Bay of Bothnia

The Cryosphere ◽

10.5194/tc-12-3459-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3459-3476 ◽

Cited By ~ 5

Author(s):

Iina Ronkainen ◽

Jonni Lehtiranta ◽

Mikko Lensu ◽

Eero Rinne ◽

Jari Haapala ◽

...

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Thickness Distribution ◽

Ice Thickness ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Thickness Variability ◽

Fast Ice ◽

Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

Download Full-text

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

Annals of Glaciology ◽

10.3189/172756406781811240 ◽

2006 ◽

Vol 44 ◽

pp. 281-287 ◽

Cited By ~ 9

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.

Download Full-text

ICESat observations of seasonal and interannual variations of sea-ice freeboard and estimated thickness in the Weddell Sea, Antarctica (2003–2009)

Annals of Glaciology ◽

10.3189/172756411795931480 ◽

2011 ◽

Vol 52 (57) ◽

pp. 43-51 ◽

Cited By ~ 52

Author(s):

Donghui Yi ◽

H. Jay Zwally ◽

John W. Robbins

Keyword(s):

Sea Ice ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Earth Observing System ◽

Pack Ice ◽

Snow Water ◽

The Mean ◽

Seasonal And Interannual Variations

AbstractSea-ice freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and sea ice, to estimate sea-ice thickness. Sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack ice. During October–November, sea ice grows to its seasonal maximum both in area and thickness; the mean freeboards are 0.33–0.41m and the mean thicknesses are 2.10–2.59 m. During February–March, thinner sea ice melts away and the sea-ice pack is mainly distributed in the west Weddell Sea; the mean freeboards are 0.35–0.46m and the mean thicknesses are 1.48–1.94 m. During May–June, the mean freeboards and thicknesses are 0.26–0.29m and 1.32–1.37 m, respectively. the 6 year trends in sea-ice extent and volume are (0.023±0.051)×106 km2 a–1 (0.45% a–1) and (0.007±0.092)×103 km3 a–1 (0.08% a–1); however, the large standard deviations indicate that these positive trends are not statistically significant.

Download Full-text

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.

Download Full-text

A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice

The Cryosphere ◽

10.5194/tc-4-583-2010 ◽

2010 ◽

Vol 4 (4) ◽

pp. 583-592 ◽

Cited By ~ 59

Author(s):

L. Kaleschke ◽

N. Maaß ◽

C. Haas ◽

S. Hendricks ◽

G. Heygster ◽

...

Keyword(s):

Sea Ice ◽

Brightness Temperature ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness ◽

Model Calculations ◽

Airborne Measurements ◽

Low Salinity ◽

Research Aircraft ◽

L Band

Abstract. In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, we investigated the potential of L-band (1.4 GHz) radiometry to measure sea-ice thickness. Sea-ice brightness temperature was measured at 1.4 GHz and ice thickness was measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea-ice. For Arctic first year ice the modelled thickness sensitivity is less than half a meter. It reduces to a few centimeters for temperatures approaching the melting point. The campaign was conducted under unfavorable melting conditions and the spatial overlap between the L-band and EM-measurements was relatively small. Despite these disadvantageous conditions we demonstrate the possibility to measure the sea-ice thickness with the certain limitation up to 1.5 m. The ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatiotemporal coverage. The relative error for the SMOS ice thickness retrieval is expected to be not less than about 20%.

Download Full-text

Seasonal development of the sea-ice thickness distribution in East Antarctica: measurements from upward-looking sonar

Annals of Glaciology ◽

10.3189/172756401781818167 ◽

2001 ◽

Vol 33 ◽

pp. 177-180 ◽

Cited By ~ 14

Author(s):

A. P. Worby ◽

G. M. Bush ◽

I. Allison

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

East Antarctica ◽

Seasonal Development ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Seasonal Evolution ◽

Pack Ice ◽

The Mean ◽

Prototype Instrument

AbstractUpward-looking sonar (ULS) data are presented from a prototype instrument deployed at 63° 18’ S, 107°49’ E in 1994. These data show the seasonal evolution of the ice-draft distribution from May when predominantly thin ice is present, through October when substantially thicker ice has been formed by deformation. The mean ice draft reaches a maximum in August at 1.21 m, the same month in which ship-based observations from the same region show a peak in ice thickness. The observed distribution from ULS data is only for drafts > 0.3 m due to data losses caused by the low acoustic reflectivity of actively forming ice. The spring distributions show very little development of drafts > 3.0 m, and it is hypothesized that this is due to the cyclical nature of deformation in the East Antarctic pack-ice zone, and that periods of sustained pressure required to form very thick ice are uncommon in this region

Download Full-text

Early detection of the Weddell polynya re-opening using SAR imagery

10.5194/egusphere-egu2020-10790 ◽

2020 ◽

Author(s):

Adriano Lemos ◽

Céline Heuzé

Keyword(s):

Early Detection ◽

Sea Ice ◽

Open Water ◽

Weddell Sea ◽

Small Scale ◽

Ice Thickness ◽

Sar Images ◽

Sea Ice Thickness ◽

Sar Imagery ◽

Microwave Sensors

<p>The sea ice thickness in the Weddell Sea during the austral winter normally exceeds 1 m, but in the case of a polynya, this thickness decreases to 10 cm or less. There are two theories as to why the Weddell Polynya opens: 1) comparatively warm oceanic water upwelling from its nominal depth of several hundred metres to the surface where it melts the sea ice from underneath; or 2) opening of a lead by a passing storm, lead which will then be maintained open either by the atmosphere or ocean and grow. The objective of this study is to estimate how long in advance the recent Weddell Polynya opening could have been detected by synthetic aperture radar (SAR) images due to the decrease of the sea ice thickness and/or early appearance of leads. We use high temporal and spatial resolution SAR images from the Sentinel-1 constellation (C-band) and ALOS2 (L-band) during the austral winters 2014-2018. We use an adapted version of the algorithm developed by Aldenhoff et al. (2018) to monitor changes in sea ice thickness over the polynya region. The algorithm detects the transition of the sea ice thickness through changes in small scale surface roughness and thus reduced backscatter, and allowing us to distinguish three different categories: ice, thin ice, and open water. The transition from ice to thin ice and then to open water indicates that the polynya is melted from under, whereas a direct transition from ice to open water will reveal leads. The high resolution and good coverage of the SAR imagery, and a combined effort of different satellites sensors (e.g. infrared and microwave sensors), opens the possibility of an early detection of Weddell Polynya opening.</p>

Download Full-text

The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

The Cryosphere ◽

10.5194/tc-9-37-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 37-52 ◽

Cited By ~ 38

Author(s):

S. Kern ◽

K. Khvorostovsky ◽

H. Skourup ◽

E. Rinne ◽

Z. S. Parsakhoo ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Ice Thickness ◽

Data Sets ◽

Snow Density ◽

Radar Altimetry ◽

Sea Ice Thickness ◽

The Mean ◽

Satellite Radar ◽

Satellite Radar Altimetry

Abstract. We assess different methods and input parameters, namely snow depth, snow density and ice density, used in freeboard-to-thickness conversion of Arctic sea ice. This conversion is an important part of sea ice thickness retrieval from spaceborne altimetry. A data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and co-locate observations of total (sea ice + snow) and sea ice freeboard from the Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) airborne campaigns, of sea ice draft from moored and submarine upward looking sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer (AMSR-E) and the Warren climatology (Warren et al., 1999). We compare the different data sets in spatiotemporal scales where satellite radar altimetry yields meaningful results. An inter-comparison of the snow depth data sets emphasizes the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. We test different freeboard-to-thickness and freeboard-to-draft conversion approaches. The mean observed ULS sea ice draft agrees with the mean sea ice draft derived from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the approaches are able to reproduce the seasonal cycle in sea ice draft observed by moored ULS. A sensitivity analysis of the freeboard-to-thickness conversion suggests that sea ice density is as important as snow depth.

Download Full-text

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.

Download Full-text