Shipborne electromagnetic measurements of Antarctic sea‐ice thickness

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1537-1546 ◽  
Author(s):  
James E. Reid ◽  
Anthony P. Worby ◽  
Julian Vrbancich ◽  
Angus I. S. Munro

We present a study of Antarctic sea‐ice thickness estimates made using a shipborne Geonics EM31 electromagnetic (EM) instrument, based on both 1D and 3D models. Apparent conductivities measured in the vertical coplanar (VCP) geometry are shown to be the measured quantity most sensitive to changes in the height of the instrument above seawater. An analysis of the effect of instrument orientation on the measured VCP apparent conductivity shows that the effects of pitch and roll on the calculated sea‐ice thickness can be neglected except in the case of very thin sea ice. Because only a single (quadrature) component of the magnetic field is measured at a single frequency, interpretation of shipborne EM31 data must necessarily be based on very simple models. For a typical sea‐ice bulk conductivity of ∼60 mS/m, a uniform half‐space model representing conductive seawater is appropriate for interpretation of VCP EM31 measurements over level sea ice up to ∼2.5 m thick. For thicker, more conductive sea ice, the interpretation model must account for the effect of the finite sea‐ice conductivity. Simultaneous acquisition of EM data at several frequencies and/or transmitter–receiver geometries permits interpretation of the data in terms of multilayered models. A synthetic example shows that 1D inversion of single‐frequency in‐phase and quadrature data from two transmitter–receiver geometries can yield reliable estimates of sea‐ice thickness even when the ice contains thin, highly conductive brine layers. Our 3D numerical model calculations show that smoothing the measured response over the system footprint means that the sea‐ice thickness recovered over multidimensional sea‐ice structures via half‐space inversion of apparent conductivity data yields a highly smoothed image of the actual keel relief. The dependence of footprint size on the height of the system above seawater results in the interpreted sea‐ice thicknesses being dependent on the deployment height of the instrument. Sea‐ice thickness data acquired using an EM31 equipped with a hardware processing module can be transformed to apparent conductivity and then inverted assuming a conductive half‐space model. For EM system heights >4.5 m above seawater, corresponding to large altitude and/or thick sea ice, inversion assuming a conductive half‐space model yields an improved estimate of the true sea‐ice thickness compared to that obtained using the processing module. However, the noise level in the estimated depth to seawater is relatively large (±0.1 m) in comparison with typical Antarctic sea‐ice thicknesses, and thickness estimates made using the shipborne system may be significantly in error over thin ice.

2003 ◽  
Vol 15 (1) ◽  
pp. 47-54 ◽  
Author(s):  
TINA TIN ◽  
MARTIN O. JEFFRIES ◽  
MIKKO LENSU ◽  
JUKKA TUHKURI

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.


2010 ◽  
Vol 4 (4) ◽  
pp. 583-592 ◽  
Author(s):  
L. Kaleschke ◽  
N. Maaß ◽  
C. Haas ◽  
S. Hendricks ◽  
G. Heygster ◽  
...  

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%.


Geophysics ◽  
1991 ◽  
Vol 56 (12) ◽  
pp. 1992-1998 ◽  
Author(s):  
Austin Kovacs ◽  
Rexford M. Morey

Field trials using a man‐portable, commercially available, electromagnetic induction (EMI) sounding instrument, with a plug‐in data processing module for the remote measurement of sea ice thickness, are discussed. The processing module was made to allow for the direct determination of sea ice thickness and to show the result in a numerical display. The processing module system was capable of estimating ice thickness within 10 percent of the the true ice value for ice from about 0.7 to 3.5 m thick, the thickest of undeformed ice in our study area. However, since seawater under the Arctic pack ice has relatively uniform conductivity (2.55 ± 0.05 S/m), a simplified method can be used for estimating sea ice thickness using just an EMI instrument. This technique uses only the EMI conductivity measurement, is easy to put into use, and does not rely on theoretically derived look‐up tables or phasor diagrams, which may not be accurate for the conditions of the area.


2013 ◽  
Vol 36 (3) ◽  
pp. 202-220 ◽  
Author(s):  
Mary D. Stampone ◽  
Cathleen A. Geiger ◽  
Tracy L. DeLiberty ◽  
E. Rachel Bernstein

2018 ◽  
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack

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.


2015 ◽  
Vol 9 (5) ◽  
pp. 4893-4923 ◽  
Author(s):  
S. Schwegmann ◽  
E. Rinne ◽  
R. Ricker ◽  
S. Hendricks ◽  
V. Helm

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.


2020 ◽  
Author(s):  
Jinfei Wang ◽  
Chao Min ◽  
Robert Ricker ◽  
Qinghua Yang ◽  
Qian Shi ◽  
...  

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.


2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.


2018 ◽  
Author(s):  
Maria Belmonte Rivas ◽  
Ines Otosaka ◽  
Ad Stoffelen ◽  
Anton Verhoef

Abstract. This paper presents the first long-term climate data record of sea ice extents and backscatter derived from inter-calibrated satellite scatterometer missions (ERS, QuikSCAT and ASCAT) extending from 1992 to present date. This record provides a valuable independent account of the evolution of Arctic and Antarctic sea ice extents, one that is in excellent agreement with the passive microwave records during the fall and winter months but shows higher sensitivity to lower concentration and melting sea ice during the spring and summer months, providing a means to correct for summer melt ponding errors. The scatterometer record also provides a depiction of sea ice backscatter at C and Ku-band, allowing the separation of seasonal and perennial sea ice in the Arctic, and further differentiation between second year (SY) and older multiyear (MY) ice classes, revealing the emergence of SY ice as the dominant perennial ice type after the record sea ice loss in 2007, and bearing new evidence on the loss of multiyear ice in the Arctic over the last 25 years. The relative good agreement between the backscatter-based sea ice (FY, SY and older MY) classes and the ice thickness record from Cryosat suggests its applicability as a reliable proxy in the historical reconstruction of sea ice thickness in the Arctic.


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