scholarly journals In situ measurements of the direct-current conductivity of Antarctic Sea ice: implications for airborne electromagnetic Sounding of Sea-ice thickness

2006 ◽  
Vol 44 ◽  
pp. 217-223 ◽  
Author(s):  
J.E. Reid ◽  
A. Pfaffling ◽  
A.P. Worby ◽  
J.R. Bishop
Keyword(s):  
Sea Ice ◽  
Direct Current ◽  
Sea Water ◽  
Vertical Direction ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Direct Current Conductivity

AbstractAirborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

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

2018 ◽  
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.

scholarly journals Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

2019 ◽  
Vol 13 (4) ◽  
pp. 1409-1422
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack ◽  
Ethan Dale
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Mcmurdo Sound ◽  
Sea Ice Thickness ◽  
Antarctic Sea 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 the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound 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 in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the 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 what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

scholarly journals An Evaluation of Antarctic Sea Ice Thickness from the Global Ice-Ocean Modeling and Assimilation System based on In-situ and Satellite Observations

2021 ◽  
Author(s):  
Sutao Liao ◽  
Hao Luo ◽  
Jinfei Wang ◽  
Qian Shi ◽  
Jinlun Zhang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Observations ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
Earth System ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
The Earth ◽  
Assimilation System

Abstract. Antarctic sea ice is an important component of the Earth system. However, its role in the Earth system is still not very clear due to limited Antarctic sea ice thickness (SIT) data. A reliable sea ice reanalysis can be useful to study Antarctic SIT and its role in the Earth system. Among various Antarctic sea ice reanalysis products, the Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output is widely used in the research of Antarctic sea ice. As more Antarctic SIT observations with quality control are released, a further evaluation of Antarctic SIT from GIOMAS is conducted in this study based on in-situ and satellite observations. Generally, though only sea ice concentration is assimilated, GIOMAS can basically reproduce the observed variability of sea ice volume and its changes in the trend before and after 2013, indicating that GIOMAS is a good option to study the long-term variation of Antarctic sea ice. However, due to deficiencies in model and asymmetric changes in SIT caused by assimilation, GIOMAS underestimates Antarctic SIT especially in deformed ice regions, which has an impact on not only the mean state of SIT but also the variability. Thus, besides the further development of model, assimilating additional sea ice observations (e.g., SIT and sea ice drift) with advanced assimilation methods may be conducive to a more accurate estimation of Antarctic SIT.

scholarly journals Thickness and density of snow-covered sea ice and hydrostatic equilibrium assumption from in situ measurements in Fram Strait, the Barents Sea and the Svalbard coast

2011 ◽  
Vol 52 (57) ◽  
pp. 261-270 ◽  
Author(s):  
Sanja Forsström ◽  
Sebastian Gerland ◽  
Christina A. Pedersen
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Sea Water ◽  
Hydrostatic Equilibrium ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
The Barents Sea ◽  
Snow And Ice

AbstractModern satellite measurements of sea-ice thickness are based on altimeter measurements of the difference in elevation between the snow or ice surface and the local sea surface. For retrieval of sea-ice thickness, it is assumed that the ice is in hydrostatic equilibrium, and that the snow load on the ice and the density of the sea ice and sea water are known. This study presents data from in situ sea-ice thickness drillings and snow and ice density measurements from Fram Strait, the Barents Sea and the Svalbard coast, in the European Arctic. the error in the altimetry ice thickness products is assessed based on the spatial variability of snow and ice density and snow thickness data. Ice thickness uncertainty related to snow depth was found to be ∼40 cm (radar altimeter) and ∼90 cm (laser altimeter), while uncertainty related to ice density is 25 cm for both techniques. the assumption of hydrostatic equilibrium at the scales of the measurements (10–100 m) was found to hold better in the case of level landfast ice near Svalbard than for Fram Strait drift ice, which consists of mixed ice types, where the deviation between the calculated and measured ice thicknesses was on average ∼0.5 m.

In-situ automatic observations of ice thickness of seas

2012 ◽  
Vol 19 (3) ◽  
pp. 583-592 ◽  
Author(s):  
Yinke Dou ◽  
Xiaomin Chang
Keyword(s):  
Sea Ice ◽  
New Technologies ◽  
Capacitive Sensor ◽  
Automatic Monitoring ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Glacier Surface ◽  
Sea Ice Thickness ◽  
Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

scholarly journals Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

2020 ◽  
Vol 12 (4) ◽  
pp. 650
Author(s):  
Pablo Sánchez-Gámez ◽  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Marcos Portabella
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
Brightness Temperature ◽  
European Space Agency ◽  
Ground Truth ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
In Situ Data ◽  
L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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

2003 ◽  
Vol 15 (1) ◽  
pp. 47-54 ◽  
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.

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

2013 ◽  
Vol 36 (3) ◽  
pp. 202-220 ◽  
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

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

2015 ◽  
Vol 9 (5) ◽  
pp. 4893-4923 ◽  
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.

scholarly journals A comparison between Envisat and ICESat sea ice thickness in the Antarctic

2020 ◽  
Author(s):  
Jinfei Wang ◽  
Chao Min ◽  
Robert Ricker ◽  
Qinghua Yang ◽  
Qian Shi ◽  
...  
Keyword(s):  
Sea Ice ◽  
European Space Agency ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Average Deviation ◽  
Absolute Deviation ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While in situ observations are too sparse in the Antarctic to determine long-term trends of the Antarctic sea ice thickness on a global scale, satellite radar altimetry data can be applied with a promising prospect. A newly released Envisat-derived product from the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI), including sea ice freeboard and sea ice thickness, covers the entire Antarctic year-round from 2002 to 2012. In this study, the SICCI Envisat sea ice thickness in the Antarctic is firstly compared with a conceptually new proposed ICESat ice thickness that has been derived from an algorithm employing modified ice density. Both data sets have been validated with the Weddell Sea upward looking sonar measurements (ULS), indicating that ICESat agrees better with field observations. The inter-comparisons are conducted for three seasons except winter based on the ICESat operating periods. According to the results, the deviations between Envisat and ICESat sea ice thickness are different considering different seasons, years and regions. More specifically, the smallest average deviation between Envisat and ICESat sea ice thickness exists in spring by −0.03 m while larger deviations exist in summer and autumn by 0.86 m and 0.62 m, respectively. Although the smallest absolute deviation occurs in spring 2005 by 0.02 m, the largest correlation coefficient appears in autumn 2004 by 0.77. The largest positive deviation occurs in the western Weddell Sea by 1.03 m in summer while the largest negative deviation occurs in the Eastern Antarctic by −0.25 m in spring. Potential reasons for those deviations mainly deduce from the limitations of Envisat radar altimeter affected by the weather conditions and the surface roughness as well as the different retrieval algorithms. The better performance in spring of Envisat has a potential relation with relative humidity.

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