Direct inference of first-year sea ice thickness using broadband acoustic backscattering

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

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

Inferring first-year sea ice thickness using broadband echosounders

The Journal of the Acoustical Society of America ◽

10.1121/1.5068024 ◽

2018 ◽

Vol 144 (3) ◽

pp. 1819-1819

Author(s):

Christopher Bassett ◽

Andone C. Lavery ◽

Jeremy P. Wilkinson ◽

Ted Maksym ◽

Zoe R. Courville

Keyword(s):

Sea Ice ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness

Download Full-text

Sea ice representation in CMIP6 simulations with EC-Earth3-Veg

10.5194/egusphere-egu2020-20372 ◽

2020 ◽

Author(s):

Torben Koenigk ◽

Evelien Dekker

Keyword(s):

Sea Ice ◽

Emission Scenario ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

First Year ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea

In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.

Download Full-text

Salinity evolution and mechanical properties of snow-loaded multiyear sea ice near an ice shelf

Antarctic Science ◽

10.1017/s0954102013000217 ◽

2013 ◽

Vol 25 (6) ◽

pp. 821-831 ◽

Cited By ~ 3

Author(s):

A.J. Gough ◽

A.R. Mahoney ◽

P.J. Langhorne ◽

T.G. Haskell

Keyword(s):

Mechanical Properties ◽

Sea Ice ◽

Strain Rate ◽

Ice Thickness ◽

First Year ◽

Ice Shelf ◽

Sea Ice Thickness ◽

Ice Shelves ◽

Complex Region ◽

Salinity Profile

AbstractSea ice often forms attached to floating ice shelves. Accumulating snow can depress its freeboard, creating a flooded slush layer that may subsequently freeze to form snow ice, rejecting brine as it freezes. The resulting salinity profile determines the mechanical properties of the sea ice. We provide measurements of snow-loaded, multiyear sea ice from summer to winter. Brine from a slush layer is not completely expelled from the sea ice when the slush refreezes to form snow ice. Measurements of sea ice salinity and temperature indicate that the fate of this brine depends on the permeability of the sea ice below it. The sea ice in this study was also deformed by a nearby ice shelf over eleven years at a strain rate $$--><$> \dot{{\epsilon}} $$$ = (-8 ± 3) × 10-4 yr-1 (or 3 × 10-11 s-1). From transects of sea ice thickness and structure we estimate an effective Young's modulus at medium scales for sea ice mostly composed of snow ice of 0.1 GPa < E < 0.4 GPa, suggesting that this eleven year old sea ice cover has similar mechanical properties to warm first year sea ice. This is important for the parameterisations needed to simulate multiyear sea ice in the complex region near an ice shelf.

Download Full-text

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.

Download Full-text

Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.2966432 ◽

2020 ◽

Vol 13 ◽

pp. 1082-1096 ◽

Cited By ~ 4

Author(s):

Vishnu Nandan ◽

Randall K. Scharien ◽

Torsten Geldsetzer ◽

Ronald Kwok ◽

John J. Yackel ◽

...

Keyword(s):

Sea Ice ◽

Case Studies ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness ◽

Ku Band

Download Full-text

Retrieval and parametrisation of sea-ice bulk density from airborne multi-sensor measurements

10.5194/tc-2021-149 ◽

2021 ◽

Author(s):

Arttu Jutila ◽

Stefan Hendricks ◽

Robert Ricker ◽

Luisa von Albedyll ◽

Thomas Krumpen ◽

...

Keyword(s):

Sea Ice ◽

Bulk Density ◽

Arctic Sea Ice ◽

Ice Thickness ◽

First Year ◽

Exponential Relationship ◽

Sea Ice Thickness ◽

Abstract Knowledge ◽

Negative Exponential ◽

The Impact

Abstract. Knowledge of sea-ice thickness and volume depends on freeboard observations from satellite altimeters and in turn on information of snow mass and sea-ice density required for the freeboard-to-thickness conversion. These parameters, especially sea-ice density, are usually based on climatologies constructed from in situ observations made in the 1980s and before while contemporary and representative measurements are lacking. Our aim with this paper is to derive updated sea-ice bulk density estimates suitable for the present Arctic sea-ice cover and a range of ice types to reduce uncertainties in sea-ice thickness remote sensing. Our sea-ice density measurements are based on over 3000 km of high-resolution collocated airborne sea-ice and snow thickness and freeboard measurements in 2017 and 2019. Sea-ice bulk density is derived assuming isostatic equilibrium for different ice types. Our results show higher average bulk densities for both first-year ice (FYI) and especially multi-year ice (MYI) compared to previous studies. In addition, we find a small difference between deformed and possibly unconsolidated FYI and younger MYI. We find a negative-exponential relationship between sea-ice bulk density and sea-ice freeboard and apply this parametrisation to one winter of monthly gridded CryoSat-2 sea-ice freeboard data. We discuss the suitability and the impact of the derived FYI and MYI bulk densities for sea-ice thickness retrievals and the uncertainty related to the indirect method of measuring sea-ice bulk density. The results suggest that retrieval algorithms be adapted to changes in sea-ice density and highlight the need of future studies to evaluate the impact of density parametrisation on the full sea-ice thickness data record.

Download Full-text

On retrieving sea ice freeboard from ICESat laser altimeter

10.5194/tc-2016-50 ◽

2016 ◽

Author(s):

Kirill Khvorostovsky ◽

Pierre Rampal

Keyword(s):

Sea Ice ◽

Sea Level ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

First Year ◽

Laser Altimeter ◽

Sea Ice Thickness ◽

The Difference ◽

Along Track

Abstract. Sea ice freeboard derived from satellite altimetry is the basis for estimation of sea ice thickness using the assumption of hydrostatic equilibrium. High accuracy of altimeter measurements and freeboard retrieval procedure are therefore required. As of today, two approaches for estimation of the freeboard using laser altimeter measurements from Ice, Cloud, and land Elevation Satellite (ICESat), referred to as tie-points (TP) and lowest-level elevation (LLE) methods, have been developed and applied in different studies. We reproduced these methods in order to assess and analyze the sources of differences found in the retrieved freeboard and corresponding thickness estimates of the Arctic sea ice as produced by the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). For the ICEsat observation periods (2003–2008) it is found that when applying the same along-track averaging scales in the two methods to calculate the local sea level references the LLE method gives significantly lower (by up to 15 cm) sea ice freeboard estimates over thick multi-year ice areas, but significantly larger estimates (by 3–5 cm in average and locally up to about 10 cm) over thin first-year ice areas, as compared to the TP method. However, we show that the difference over first-year ice areas can be reduced to less than 2 cm when using the improved TP method proposed in this paper. About 4 cm of the difference in the JPL and GSFC freeboard estimates can be attributed to the different along-track averaging scales used to calculate the local sea level references. We show that the effect of applying corrections for lead width relative to the ICESat footprint, and for snow depth accumulated in refrozen leads (as it is done for the last release of the JPL product), is very large and increase freeboard estimates by about 7 cm. Thus, the different along-track averaging scales and approaches to calculate sea surface references, from one side, and the freeboard adjustments as applied in the TP method used to produce the JPL dataset, from the other side, are roughly compensating each other with respect to freeboard estimation. Therefore the difference in the mean sea ice thickness found between the JPL and GSFC datasets should be attributed to different parameters used in the freeboard-to-thickness conversion.

Download Full-text

Evaluation of Sea-Ice Thickness from four reanalyses in the Antarctic Weddell Sea

10.5194/tc-2020-71 ◽

2020 ◽

Cited By ~ 1

Author(s):

Qian Shi ◽

Qinghua Yang ◽

Longjiang Mu ◽

Jinfei Wang ◽

François Massonnet ◽

...

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

Weddell Sea ◽

Ocean Modeling ◽

Ice Thickness ◽

First Year ◽

Coupled Models ◽

Sea Ice Thickness ◽

The Antarctic

Abstract. Ocean-sea ice coupled models constrained by varied observations provide different ice thickness estimates in the Antarctic. We evaluate contemporary monthly ice thickness from four reanalyses in the Weddell Sea, the German contribution of the Estimating the Circulation and Climate of the Ocean project, Version 2 (GECCO2), the Southern Ocean State Estimate (SOSE), the Nucleus for European Modelling of the Ocean (NEMO) based ocean-ice model (called NEMO-EnKF), and the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), and with reference observations from ICESat-1, Envisat, upward looking sonars and visual ship-based sea-ice observations. Compared with ICESat-1 altimetry and in situ observations, all reanalyses underestimate ice thickness near the coast of the western Weddell Sea, even though ICESat-1 and visual observations may be biased low. GECCO2 and NEMO-EnKF can well reproduce the seasonal variation of first-year ice thickness in the eastern Weddell Sea. In contrast, GIOMAS ice thickness performs best in the central Weddell Sea, while SOSE ice thickness agrees most with the observations in the southern coast of the Weddell Sea. In addition, only NEMO-EnKF can reproduce the seasonal spatial evolution of ice thickness distribution well, characterized by the thick ice shifting from the southwestern and western Weddell Sea in summer to the western and northwestern Weddell Sea in spring. We infer that the thick ice distribution is correlated with its better simulation of northward ice motion in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis for understanding the recent variability of sea-ice volume in the Antarctic.

Download Full-text