Sea-ice thickness and roughness in the Ross Sea, Antarctica

AbstractSea-ice thickness and roughness data collected on three cruises in the Ross Sea, Antarctica, showed interseasonal, regional and interannual variability. Variability was reduced to season, or age of ice floe, when sea-ice roughness values from around Antarctica were compared. There were statistically significant correlations between mean snow elevation and mean ice thickness; snow surface roughness and mean ice thickness; and snow surface roughness and ice bottom roughness, which appeared to be independent of season, geographical location and deformation history of ice floes. Our field data indicate that ice thickness can be predicted from snow elevation measurements with higher accuracy in summer. The feasibility of using snow surface roughness to infer ice thickness and ice bottom roughness is promising, and can provide us with a means to study the thickness and underside of Antarctic sea ice at good spatial and temporal resolution.

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Estimating the thickness of ridged sea ice from ship observations in the Ross Sea

Antarctic Science ◽

10.1017/s0954102003001056 ◽

2003 ◽

Vol 15 (1) ◽

pp. 47-54 ◽

Cited By ~ 5

Author(s):

TINA TIN ◽

MARTIN O. JEFFRIES ◽

MIKKO LENSU ◽

JUKKA TUHKURI

Keyword(s):

Sea Ice ◽

Ross Sea ◽

Low Cost ◽

Thickness Distribution ◽

Ice Thickness ◽

Equivalent Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Using Data ◽

Original Formula

Ship-based observations of sea ice thickness using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol provide information on ice thickness distribution at relatively low cost. This protocol uses a simple formula to calculate the mass of ice in ridges based on surface observations. We present two new formulae and compare these with results from the “Original” formula using data obtained in the Ross Sea in autumn and winter. The new “r-star” formula uses a more realistic ratio of sail and keel areas to transform dimensions of sails to estimates of mean keel areas. As a result, estimates of “equivalent thickness” (i.e. mean thickness of ice in ridged areas) increased by over 200%. The new “Probability” formula goes one step further, by incorporating the probability that a sail is associated with a keel underwater, and the probability that keels may be found under level surfaces. This resulted in estimates of equivalent thickness comparable with the Original formula. Estimates of equivalent thickness at one or two degree latitude resolution are sufficiently accurate for validating sea ice models. Although ridges are small features in the Ross Sea, we have shown that they constitute a significant fraction of the total ice mass.

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A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

Remote Sensing ◽

10.3390/rs12091494 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1494

Author(s):

M. Jeffrey Mei ◽

Ted Maksym

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Thickness Distribution ◽

Radar Data ◽

Weddell Sea ◽

Ice Thickness ◽

Two Dimensional ◽

Snow Surface ◽

Sea Ice Thickness ◽

Antarctic Sea Ice

The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

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A textural approach to snow depth distribution on Antarctic sea ice

10.5194/egusphere-egu2020-1167 ◽

2020 ◽

Author(s):

M. Jeffrey Mei ◽

Ted Maksym

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Ice Thickness ◽

Two Dimensional ◽

Snow Surface ◽

Altimetry Data ◽

Laser Altimetry ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Surface Morphologies

Understanding the distribution of snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2.&#160; One major uncertainty in converting laser altimetry data to ice thickness is knowing the proportion of snow within the surface measurement. Snow redistributed by wind collects around areas of deformed ice, but it is not known how different surface morphologies affect this distribution. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow-ice ratios using snow surface freeboard measurements from Operation IceBridge (OIB) campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, but not similar snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth. Using a convolutional neural network on an in-situ dataset, we find that local (~20 m) snow depth and sea ice thickness can be estimated with errors of < 20%, and that the learned convolutional filters imply that different surface morphologies have different proportions of snow/ice within the measured surface elevation. For the OIB data,&#160; we show that at slightly larger scales (~180 m), snow depths can be estimated using the snow surface texture, and that the learned filters are comparable to standard textural segmentation filters. We also examine the statistical variability in the distribution of snow/ice ratios across different years to determine if snow distribution patterns on sea ice exhibit universal behaviour, or have significant interannual variations. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, compared to current methods. Such methods may be useful for reducing errors in Antarctic sea ice thickness estimates from ICESat-2.

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Effects of decimetre-scale surface roughness on L-band Brightness Temperature of Sea Ice

10.5194/tc-2019-110 ◽

2019 ◽

Author(s):

Maciej Miernecki ◽

Lars Kaleschke ◽

Nina Maaß ◽

Stefan Hendricks ◽

Sten Schmidl Søbjrg

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Brightness Temperature ◽

Large Scale ◽

Ice Thickness ◽

Emission Model ◽

Vertical Polarization ◽

Sea Ice Thickness ◽

L Band ◽

Scale Surface

Abstract. Sea ice thickness measurements with L-band radiometry is a technique which allows daily, weather-independent monitoring of the polar sea ice cover. The sea-ice thickness retrieval algorithms relay on the sensitivity of the L-band brightness temperature to sea-ice thickness. In this work, we investigate the decimetre-scale surface roughness as a factor influencing the L-band emissions from sea ice. We used an airborne laser scanner to construct a digital elevation model of the sea ice surface. We found that the probability density function of surface slopes is exponential for a range of degrees of roughness. Then we applied the geometrical optics, bounded with the MIcrowave L-band LAyered Sea ice emission model in the Monte Carlo simulation to simulate the effects of surface roughness. According to this simulations, the most affected by surface roughness is the vertical polarization around Brewster's angle, where the decrease in brightness temperature can reach 8 K. The vertical polarization for the same configuration exhibits a 4 K increase. The near-nadir angles are little affected, up to 2.6 K decrease for the most deformed ice. Overall the effects of large-scale surface roughness can be expressed as a superposition of two factors: the change in intensity and the polarization mixing. The first factor depends on surface permittivity, second shows little dependence on it. Comparison of the brightness temperature simulations with the radiometer data does not yield definite results.

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Sea ice deformation and thickness in the Western Ross Sea

10.5194/egusphere-egu21-10607 ◽

2021 ◽

Author(s):

Wolfgang Rack ◽

Daniel Price ◽

Christian Haas ◽

Patricia J. Langhorne ◽

Greg H. Leonard

Keyword(s):

Sea Ice ◽

Satellite Images ◽

Ross Sea ◽

Thickness Distribution ◽

Ice Thickness ◽

Ice Dynamics ◽

Sea Ice Thickness ◽

Ice Volume ◽

Terra Nova Bay ◽

Western Ross Sea

Sea ice cover is arguably the longest and best observed climate variable from space, with over four decades of highly reliable daily records of extent in both hemispheres. In Antarctica, a slight positive decadal trend in sea ice cover is driven by changes in the western Ross Sea, where a variation in weather patterns over the wider region forced a change in meridional winds. The distinguishing wind driven sea ice process in the western Ross Sea is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Trends in sea ice volume and mass in this area unknown, because ice thickness and dynamics are particularly hard to measure.Here we present the first comprehensive and direct assessment of large-scale sea-ice thickness distribution in the western Ross Sea. Using an airborne electromagnetic induction (AEM) ice thickness sensor towed by a fixed wing aircraft (Basler BT-67), we observed in November 2017 over a distance of 800 km significantly thicker ice than expected from thermodynamic growth alone. By means of time series of satellite images and wind data we relate the observed thickness distribution to satellite derived ice dynamics and wind data. Strong southerly winds with speeds of up to 25 ms-1 in early October deformed the pack ice, which was surveyed more than a month later.We found strongly deformed ice with a mean and maximum thickness of 2.0 and 15.6 m, respectively. Sea-ice thickness gradients are highest within 100-200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m. From comparison with aerial photographs and satellite images we conclude that ice preferentially grows in deformational ridges; about 43% of the sea ice volume in the area between McMurdo Sound and Terra Nova Bay is concentrated in more than 3 m thick ridges which cover about 15% of the surveyed area. Overall, 80% of the ice was found to be heavily deformed and concentrated in ridges up to 11.8 m thick.Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea. The sea ice statistics highlighted in this contribution forms a basis for improved satellite derived mass balance assessments and the evaluation of sea ice simulations.

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Retrieval of sea-ice thickness distribution in the Sea of Okhotsk from ALOS/PALSAR backscatter data

Annals of Glaciology ◽

10.3189/172756411795931732 ◽

2011 ◽

Vol 52 (57) ◽

pp. 177-184 ◽

Cited By ~ 7

Author(s):

Takenobu Toyota ◽

Shuji Ono ◽

Kohei Cho ◽

Kay I. Ohshima

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Sea Of Okhotsk ◽

Thickness Distribution ◽

Ice Thickness ◽

Alos Palsar ◽

Sea Ice Thickness ◽

Promising Tool ◽

L Band ◽

Interferometric Sar

AbstractAlthough satellite data are useful for obtaining ice-thickness distribution for perennial sea ice or in stable thin-sea-ice areas, they are still largely an unresolved issue for the seasonal ice zone (SIZ). We address this problem using L-band synthetic aperture radar (SAR). In the SIZ, ice-thickness growth is closely related to deformation, so surface roughness is expected to correlate with ice thickness. L-band SAR, suitable for detecting such surface roughness, is a promising tool for obtaining thickness distribution. This idea was supported by an airborne polarimetric and interferometric SAR (Pi-SAR) validation. To extend this result to spaceborne L-band SAR with coarser resolution, we conducted in situ measurements of ice thickness and surface roughness in February 2008 in the southern Sea of Okhotsk with an icebreaker in coordination with the Advanced Land Observing Satellite (ALOS)/Phased Array-type L-band SAR (PALSAR) orbit. A helicopter-borne laser profiler was used to improve the estimation of surface roughness. It was found that backscatter coefficients (HH) correlated well with ice thickness (R = 0.86) and surface roughness (R = 0.70), which confirms the possibility of determining ice-thickness distribution in the SIZ. the interannual variation of PALSAR-derived ice-thickness distribution in the southern Sea of Okhotsk is also discussed.

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Data-derived spatial-resolution errors of Antarctic sea-ice thickness

Polar Geography ◽

10.1080/1088937x.2012.691120 ◽

2013 ◽

Vol 36 (3) ◽

pp. 202-220 ◽

Cited By ~ 1

Author(s):

Mary D. Stampone ◽

Cathleen A. Geiger ◽

Tracy L. DeLiberty ◽

E. Rachel Bernstein

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice

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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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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

The Cryosphere Discussions ◽

10.5194/tcd-9-4893-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 4893-4923 ◽

Cited By ~ 1

Author(s):

S. Schwegmann ◽

E. Rinne ◽

R. Ricker ◽

S. Hendricks ◽

V. Helm

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Altimeter Data ◽

Ice Thickness ◽

Radar Altimeter ◽

Growth Season ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Abstract Knowledge ◽

Modal Values

Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, data come from different sources, and the characteristics of individual sensors differ. Hence, it is important to study the consistency between single sensors in order to develop long and consistent time series over the potentially available measurement period. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on-board Envisat and freeboard measurements from the Synthetic-Aperture Interferometric Radar Altimeter on-board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are comparable over the sea-ice growth season (May–October) and partly also beyond. In general, Envisat data shows higher freeboards in the seasonal ice zone while CryoSat-2 freeboards are higher in the perennial ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regional separated) Southern Ocean differs generally by not more than 2 cm (5 cm, except for the Amundsen/Bellingshausen Sea) between Envisat and CryoSat-2, and the differences between modal freeboard lie generally within ±10 cm and often even below.

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